Method and apparatus for detecting seizures

ABSTRACT

A method of detecting seizures may comprise receiving an EMG signal and processing the received EMG signal to determine whether a seizure characteristic is present in the EMG signal during a time window. An apparatus for detecting seizures with motor manifestations may comprise one or more EMG electrodes capable of providing an EMG signal substantially representing seizure-related muscle activity; and a processor configured to receive the EMG signal, process the EMG signal to determine whether a seizure may be occurring, and generate an alert if a seizure is determined to be occurring based on the EMG signal.

PRIORITY DATA

This application is a continuation of U.S. patent application Ser. No.13/275,309 filed Oct. 17, 2011 and claims priority from U.S. ProvisionalApplication No. 61/393,747, filed Oct. 15, 2010, the disclosures ofwhich are each incorporated herein by reference.

BACKGROUND

A seizure may be characterized as abnormal or excessive synchronousactivity in the brain. At the beginning of a seizure, neurons in thebrain may begin to fire at a particular location. As the seizureprogresses, this firing of neurons may spread across the brain, and insome cases, many areas of the brain may become engulfed in thisactivity. Seizure activity in the brain may cause the brain to sendelectrical signals through the peripheral nervous system to differentmuscles. For example, an electrical signal may originate in the centralnervous system and initiate the propagation of an electrical signalthrough motor neurons. A motor neuron may, for example, communicate witha muscle through interaction with the motor end plate of a muscle fiber;thereby initiating an action potential and depolarization of musclecells within a given motor unit. Depolarization typically results fromthe coordinated flow of ions, e.g., sodium and potassium cations,through channels within a muscle cell membrane. That is, changes instates of ion channels initiate a change in the permeability of a cellmembrane, and subsequent redistribution of charged ions. Current flowthrough muscle cells may initiate a corresponding flow in the tissueabove the muscle and thus an electrical signature at the surface of theskin.

Techniques designed for studying and monitoring seizures have typicallyrelied upon electroencephalography (EEG), which characterizes electricalsignals using electrodes attached to the scalp or head region of aseizure prone individual, or seizure patient. EEG electrodes may bepositioned so as to measure such activity, that is, electrical activityoriginating from neuronal tissue. Compared to EEG, electromyography(EMG) is a little-used technique in which an electrode may be placed onor near the skin, over a muscle, to detect an electrical current orchange in electric potential in response to redistribution of ionswithin muscle fibers.

Detecting an epileptic seizure using electroencephalography (EEG)typically requires attaching many electrodes and associated wires to thehead and using amplifiers to monitor brainwave activity. The multipleEEG electrodes may be very cumbersome and generally require sometechnical expertise to apply and monitor. Furthermore, confirming aseizure requires observation in an environment provided with videomonitors and video recording equipment. Unless used in a staffedclinical environment, such equipment is frequently not intended todetermine if a seizure is in progress but rather provide a historicalrecord of the seizure after the incident. Such equipment is usuallymeant for hospital-like environments where a video camera recording orcaregiver's observation may provide corroboration of the seizure, and istypically used as part of a more intensive care regimen such as ahospital stay for patients who experience multiple seizures. A hospitalstay may be required for diagnostic purposes or to stabilize a patientuntil suitable medication can be administered. Upon discharge from thehospital, a patient may be sent home with little further monitoring.However, at any time after being sent home the person may experienceanother seizure, perhaps fatal.

A patient should in some cases be monitored at home for some length oftime in case another seizure should occur. Seizures with motormanifestations may have patterns of muscle activity that includerhythmic contractions of some, most, or all of the muscles of the body.A seizure could, for example, result in Sudden Unexplained Death inEpilepsy (SUDEP). The underlying causes of SUDEP are not wellunderstood; however, some possible mechanisms causing SUDEP may includetonic activation of the diaphragm muscle so as to prevent breathing,neurogenic pulmonary edema, asystole, and other cardiac dysrhythmia. Ifa sleeping person experiences a seizure involving those conditions, thencaregivers may not be aware that the seizure is occurring, and thus beunable to render timely aid.

While there presently exist ambulatory devices for diagnosis ofseizures, they are EEG-based and are generally not designed or suitablefor long-term home use or daily wearability. Other seizure alertingsystems may operate by detecting motion of the body, usually theextremities. Such systems may generally operate on the assumption thatwhile suffering a seizure, a person will move erratically and violently.For example, accelerometers may be used to detect violent extremitymovements. However, depending upon the type of seizure, this assumptionmay or may not be true. Electrical signals sent from the brain duringthe seizure are frequently transmitted to many muscles simultaneously,which may result in muscles fighting each other and effectivelycanceling out violent movement. In other words, the muscles may work tomake the person rigid rather than cause actual violent movement. Thus,the seizure may not be consistently detected with accelerometer-baseddetectors.

Accordingly, there is a need for an epileptic seizure detection methodand apparatus that can be used in a non-institutional or institutionalenvironment without many of the cumbersome electrodes to the head orextremities. Such an apparatus may be minimally intrusive, minimallyinterfere with daily activities and be comfortably used while sleeping.There is also a need for an epileptic seizure detection method andapparatus that accurately detects a seizure with motor manifestationsand may alert one or more local and/or remote sites of the presence of aseizure. Furthermore, there is a need for an epileptic seizure detectionmethod and apparatus that may be used in a home setting and which mayprovide robust seizure detection, even in the absence of violent motion,and which may be personalizable, e.g., capable of being tailored for anindividual or specific population demographic.

SUMMARY

In some embodiments, a method of detecting seizures may comprisereceiving an EMG signal and processing the received EMG signal todetermine whether a seizure characteristic is present in the EMG signalduring a time window.

In some embodiments, an apparatus for detecting seizures with motormanifestations may comprise one or more EMG electrodes capable ofproviding an EMG signal substantially representing seizure-relatedmuscle activity; and a processor configured to receive the EMG signal,process the EMG signal to determine whether a seizure may be occurring,and generate an alert if a seizure is determined to be occurring basedon the EMG signal.

In some embodiments, apparatuses and methods comprise a detection unitwhich includes EMG electrodes and a base unit in communication andphysically separated from said detection unit, wherein the base stationis configured for receiving and processing EMG signals from thedetection unit, determining from the processed EMG signals whether aseizure may have occurred, and sending an alert to at least onecaregiver. In some embodiments, the base station may separately processthe data provided by the detection unit for verification of the alarmcondition. If the base station agrees with the alarm, then the basestation may generate an alarm to remote devices and local soundgenerators. Having the base station agree to the detection unit's alarmmay introduce a voting concept. Both devices must vote on the decisionand agree to sound the alarm. This may be used to limit false alarms.

In some embodiments, a method and apparatus for detecting a seizure andproviding a remote warning of that incident is provided. Such a methodmay detect seizures using EMG electrodes. One or more EMG electrodes maybe attached to an individual's body and one or more characteristics fromthe signal output of the one or more EMG electrodes may be analyzed. EMGoutput may be compared to general seizure characteristics and to one ormore threshold values. If one or more values of the output data exceedone or more thresholds an event may be registered, e.g., logged on aregister. Analysis of events logged in registers for differentcharacteristics of the output data may be used to assess whether aseizure incident is declared and whether an alarm is sent to one or morelocations.

In some embodiments, an apparatus for detecting seizures with motormanifestations may include a detector unit and a base unit. The detectorunit may include one or more electromyography (EMG) electrodes, andoptionally one or more electrocardiography (ECG) electrodes. Thedetector unit and base unit may be in communication with each other,such as by wireless communication. The detector unit and base unit mayinclude electronic components configured to execute instructions forevaluation of EMG signal data. The base unit may be enabled for sendingan alarm to one or more remote locations. Alternatively, the base unitmay be in communication with a separate transceiver. That transceivermay be physically distinct but within the general locale of the baseunit. That transceiver may be enabled for sending an alarm to one ormore remote locations.

In some embodiments, an alarm protocol may be initiated based on aconvolution of data in a plurality of data registers. Individualregisters may, for example, each be responsive to detection of adifferent seizure variable. An alarm protocol may be initiated if asupervisory algorithm, that supervisory algorithm responsive to thevalues in the plurality of registers, determines that an alarm protocolshould be initiated.

In some embodiments, seizure detection methods as described herein maybe adaptive. For example, threshold values may be adjusted as seizuredata is collected from one or more patients. In addition, algorithms,which may be used to determine whether a seizure incident is declared,may be modified. Algorithms may, for example, be modified by adjustingvariable coefficients. Those coefficients may be associated with, andweight, seizure variables. The adjustment of such coefficients may bebased on seizure data that is collected from one or more patients,including, but not limited to an individual patient, or other patients,such as those of a particular demographic. The association betweenregistered events, the initiation of alarm protocols, and seizurerelated incidents, e.g., declared events, actual seizures andinaccurately reported incidents, may be tracked and used to updatevariables in a detection method and thus improve the accuracy of aseizure detection method or apparatus.

In some embodiments, a historical record of patient seizure data andrelated incidents may be collected. A user may analyze a historicalrecord and modify or change one or more sub-methods or alter thedistribution of sub-methods that are included in a method for detectinga seizure. A sub-method may, for example, be a set of instructions whichmay be used to increment a counter. Sub-methods may include data,including for example, threshold values, weighting coefficients andother data, may be provided in a template file, may have a “factorydefault” setting, and may change as the method adapts to a particularpatient.

In some embodiments, the value of a plurality of seizure variables maybe determined for a patient. Individual seizure variables may beselected and analyzed using algorithms such that events logged for anindividual seizure variable is unlikely to trigger an alarm; however,the convolution of events logged for the plurality of seizure variablesmay raise the confidence with which a seizure may be detected.

In some embodiments, a method and apparatus may be used, for example, toinitiate an alarm protocol, create a log of seizure incidents to helpmedically or surgically manage the patient, activate a Vagal NerveStimulator, or activate other stimulating devices that may be used toabort or attenuate a seizure. In some embodiments, a log of seizurerelated incidents may prompt a physician to understand more quickly thefailure of a treatment regimen.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a seizure detection system.

FIG. 2 illustrates one embodiment of a detection unit and base stationfor a seizure detection system.

FIG. 3 illustrates one embodiment of a base station.

FIG. 4 illustrates one embodiment of a method for detecting seizurerelated incidents.

FIG. 5A and FIG. 5B illustrate exemplary EMG time domain data for apatient.

FIG. 6A and FIG. 6B illustrate exemplary EMG frequency domain data for apatient.

FIG. 7 illustrates one embodiment of a burst detection algorithm.

FIG. 8A and FIG. 8B illustrate exemplary model forms or envelopes ofsignal bursts after filtering, rectification and peak detection.

FIGS. 9A, 9B and 9C illustrate another embodiment of a burst and bursttrain detection algorithm

FIG. 10 illustrates one embodiment of a periodicity algorithm.

FIG. 11 illustrates one embodiment of a GTC waveform detectionalgorithm.

FIG. 12 illustrates a second embodiment of a GTC waveform detectionalgorithm.

FIG. 13 illustrates one embodiment of a waveform regularity detectionalgorithm.

FIG. 14 illustrates one embodiment of a supervisory algorithm.

FIG. 14A illustrates another embodiment of a supervisory algorithm.

FIG. 15 illustrates one embodiment of a method of data collection.

FIG. 16 illustrates one embodiment of a method of updating a templatefile.

FIG. 17 illustrates one embodiment of a method of adjusting the state ofa detection unit in a method of seizure monitoring.

FIG. 18 illustrates one embodiment of an amplitude detection algorithm.

FIG. 19 illustrates a further embodiment of a method for detectingseizure related incidents.

FIG. 20 illustrates a still further embodiment of a method for detectingseizure related incidents.

FIG. 21 illustrates how model data in a procedure for analysis of databursts may be organized.

FIG. 22 illustrates how model data for analysis of data bursts iscombined with data from a GTC accumulation register and how data inthose registers may be analyzed in a supervisory algorithm.

FIG. 23 illustrates exemplary EMG electrical data for a patient.

FIG. 24 illustrates exemplary EMG electrical data for a patient whilenon-seizure moving.

FIG. 25 illustrates exemplary EMG electrical data for a patient who issleeping.

FIG. 26 illustrates exemplary EMG electrical data for a patient at theonset of a seizure.

FIG. 27 illustrates exemplary EMG electrical data for a patient as theseizure progresses.

FIG. 28 illustrates exemplary EMG electrical data for a patient that hasbeen filtered.

FIG. 29 illustrates further exemplary EMG electrical data for a patientthat has also been filtered.

FIG. 30 illustrates the same exemplary EMG electrical data as shown inFIG. 29 and filtered using a different filter protocol.

FIG. 31 illustrates exemplary EMG electrical data for a patient showingshort-lived data events.

FIG. 32 illustrates still further exemplary EMG electrical data for apatient that has been filtered.

FIG. 33 illustrates exemplary EMG electrical data for a patient showingsustained signals.

FIG. 34 illustrates another exemplary EMG electrical data for a patientthat has been filtered.

FIG. 35 illustrates another exemplary EMG electrical data for a patient.

FIG. 36 illustrates yet another exemplary EMG electrical data.

DETAILED DESCRIPTION

The apparatuses and methods described herein may be used to detectseizures and timely alert caregivers of a seizure using EMG, among otherthings. The apparatuses and method may be used, for example, to initiatean alarm protocol, create a log of seizure incidents to help medicallyor surgically manage the patient, activate a Vagal Nerve Stimulator, oractivate other stimulating devices that may be used to abort orattenuate a seizure. In some embodiments, a log of seizure relatedincidents may prompt a physician to understand more quickly the failureof a treatment regimen. The apparatuses and methods may comprise aprocess and device and/or system of devices for detecting seizures withmotor manifestations including, but not limited to Tonic-Clonic,Tonic-only, or Clonic-only seizures. A “motor manifestation” may in someembodiments generally refer to muscle activity, whether sustained orotherwise.

Apparatuses as described herein may be useful for monitoring a person todetermine whether the person may be having a seizure, and for initiatingan alarm. The methods described herein may be flexible, e.g., suchmethods may be customized for an individual. Moreover, such methods maybe adaptive, and may improve as data is collected, e.g., for a givenpatient or for a certain patient demographic. Furthermore, apparatusesdescribed herein may be suited for organizing and/or prioritizing thecollection of large amounts of data, e.g., data that may be collected ina substantially continuous manner, such as while a seizure-proneindividual is in a home setting.

In general terms, EMG electrode signals may be collected and processedto determine seizure variables. A “seizure variable” may in someembodiments refer to a criterion or criteria of one or more portions ofdata collected from the output signal of a detector. For a given set ofdata, a seizure variable may have one or more numerical valuesassociated with it. For example, the amplitude of a signal may be aseizure variable that may have one or more numerical values associatedwith it for a given set of data. A value of a seizure variable may becompared to a threshold level and may be used as an input in analgorithm for determining whether a seizure may have occurred.

A processing method may include calculating one or more seizure variablevalues and may further include comparing such values to one or morethresholds that may characterize a seizure. Data registers may bepopulated based upon such a comparison, and used to evaluate whether toinitiate an alarm protocol. The weighting of data in differentregisters, and thus the importance of different characteristics of EMGdata, may be customized for an individual patient or patientdemographic, and may adapt as the system obtains more information for apatient or patient demographic.

A variety of suitable systems may be suitable for collecting largeamounts of EMG and other patient-related data, organizing such data forsystem optimization, and for initiating an alarm in response to asuspected seizure. FIG. 1 illustrates an exemplary embodiment of such asystem. In the embodiment of FIG. 1, a seizure detection system 10 mayinclude a detection unit 12, an optional base station 14, an optionalvideo monitor 9 and an optional alert transceiver 16. The detection unitmay comprise one or more EMG electrodes capable of detecting electricalsignals from muscles at or near the skin surface of a patient, anddelivering those electrical EMG signals to a processor for processing.The base station may comprise a computer capable of receiving andprocessing EMG signals from the detection unit, determining from theprocessed EMG signals whether a seizure may have occurred, and sendingan alert to a caregiver. An alert transceiver may be carried by, orplaced near, a caregiver to receive and relay alerts transmitted by thebase station.

In using the apparatus of FIG. 1, for example, a person 11 susceptibleto epileptic seizures may be resting in bed, or may be at some otherlocation as daily living may include, and may have a detection unit 12in physical contact with or in proximity to his or her body. Thedetection unit 12 may be a wireless device so that a person may be ableto get up and walk around without having to be tethered to an immobilepower source or to a bulkier base station 14. For example, the detectionunit 12 may be woven into a shirt sleeve, or may be mounted to anarmband or bracelet. In other embodiments, one or more detection units12 may be placed or built into a bed, a chair, an infant car seat, orother suitable clothing, furniture, equipment and accessories used bythose susceptible to seizures. The detection unit 12 may comprise asimple sensor, such as an electrode, that may send signals to the basestation for processing and analysis, or may comprise a “smart” sensorhaving some data processing and storage capability. In some embodiments,a simple sensor may be connected via wire or wirelessly to abattery-operated transceiver mounted on a belt worn by the person.

The system may monitor the patient, for example, while resting, such asduring the evening and nighttime hours. If the detection unit 12 on thepatient detects a seizure, the detection unit 12 may communicate viawire or wirelessly, e.g., via a communications network or wireless link,with the base station 14 and may send some signals to the base stationdevice for more thorough analysis. For example, the detection unit 12may process and use EMG signals (and optionally ECG and temperaturesensor signals) to make an initial assessment regarding the likelihoodof occurrence of a seizure, and may send those signals and itsassessment to the base station 14 for separate processing andconfirmation. If the base station 14 confirms that a seizure is likelyoccurring, then the base station 14 may initiate an alarm fortransmission over the network 15 to alert a caregiver by way of email,text, or any suitable wired or wireless messaging indicator. In someembodiments, if one or more of the detection unit 12, the base station14, or a caregiver, e.g., a remotely located caregiver monitoringsignals provided from the base station, determines that a seizure may beoccurring, a video monitor 9 may be triggered to collect information.

The base station 14, which may be powered by a typical household powersupply and contain a battery for backup, may have more processing,transmission and analysis power available for its operation than thedetection unit 12, may be able to store a greater quantity of signalhistory, and evaluate a received signal against that greater amount ofdata. The base station 14 may communicate with an alert transceiver 16located remotely from the base station 14, such as in the bedroom of afamily member, or to a wireless device 17, 18 carried by a caregiver orlocated at a work office or clinic. The base station 14 and/ortransceiver 16 may send alerts or messages to caregivers, or medicalpersonnel via any suitable means, such as through a network 15 to a cellphone 17, personal digital assistant (PDA) 18 or other client device.The system 10 may thus provide an accurate log of seizures, which mayallow a patient's physician to understand more quickly the success orfailure of a treatment regimen. Of course, the base station 14 maysimply comprise a computer having installed a program capable ofreceiving, processing and analyzing signals as described herein, andcapable of transmitting an alert. In other embodiments, the system 10may simply comprise, for example, EMG electrodes and a smartphone, suchas an iPhone, configured to receive EMG signals from the electrodes forprocessing the EMG signals as described herein using an installedprogram application. In further embodiments, so-called “cloud” computingand storage may be used via network 15 for storing and processing theEMG signals and related data. In yet other embodiments, one or more EMGelectrodes could be packaged together as a single unit with a processorcapable of processing EMG signals as disclosed herein and sending analert over a network. In other words, the apparatus may comprise asingle item of manufacture that may be placed on a patient and that doesnot require a base station separate transceiver.

In the embodiment of FIG. 1, the signal data may be sent to a remotedatabase 19 for storage. In some embodiments, signal data may be sentfrom a plurality of epileptic patients to a central database 19 and“anonymized” to provide a basis for establishing and refininggeneralized “baseline” sensitivity levels and signal characteristics ofan epileptic seizure. The database 19 and base station 14 may beremotely accessed via network 15 by a remote computer 13 to allowupdating of detector unit and/or base station software, and datatransmission. The base station 14 may generate an audible alarm, as maya remote transceiver 16. All wireless links may be two-way for softwareand data transmission and message delivery confirmation. The basestation 14 may also employ one or all of the messaging methods listedabove for seizure notification. The base station 14 may provide an“alert cancel” button to terminate the incident warning.

In some embodiments, a transceiver may additionally be mounted within aunit of furniture or some other structure, e.g., an environmental unitor object. If a detection unit is sufficiently close to thattransceiver, such a transceiver may be capable of sending data to a basestation. Thus, the base station may be aware that information is beingreceived from that transducer, and therefore the associatedenvironmental unit. In some embodiments, a base station may select aspecific template file, e.g., such as including threshold values andother data as described further herein, that is dependent upon whetheror not it is receiving a signal from a certain transceiver. Thus, forexample, if the base station receives information from a detector andfrom a transducer that is associated with a bed or crib it may treat thedata differently than if the data is received from a transducerassociated with another environmental unit, such as, for example,clothing typically worn while an individual may be exercising

The embodiment of FIG. 1 may be configured to be minimally intrusive touse while sleeping or minimally interfere in daily activities, mayrequire a minimum of electrodes such as one or two, may require noelectrodes to the head, may detect a seizure with motor manifestations,may alert one or more local and/or remote sites of the presence of aseizure, and may be inexpensive enough for home use.

FIG. 2 illustrates an embodiment of a detection unit 12 or detector. Thedetection unit 12 may include EMG electrodes 20, and may also includeECG electrodes 21. The detection unit 12 may further include amplifierswith leads-off detectors 22. In some embodiments, one or more leads-offdetectors may provide signals that indicate whether the electrodes arein physical contact with the person's body, or otherwise too far fromthe person's body to detect muscle activity, temperature, brain activityor other patient phenomena.

The detection unit 12 may further include a temperature sensor 23 tosense the person's temperature. Other sensors (not shown) may beincluded in the detection unit as well, such as accelerometers. Signalsfrom electrodes 20 and 21, temperature sensor 23 and other sensors maybe provided to a multiplexor 24. The multiplexor 24 may be part of thedetection unit 12 or may be part of the base station 14 if the detectionunit 12 is not a smart sensor. The signals may then be communicated fromthe multiplexor 24 to one or more analog-to-digital (A-D) converters 25.The analog-to-digital converters may be part of the detection unit 12 ormay be part of the base station 14. The signals may then be communicatedto one or more microprocessors 26 for processing and analysis asdisclosed herein. The microprocessors 26 may be part of the detectionunit 12 or may be part of the base station 14. The detection unit 12and/or base station 14 may further include memory of suitable capacity.The microprocessor 26 may communicate signal data and other informationusing a transceiver 27. Communication by and among the components of thedetection unit 12 and/or base station 14 may be via wired or wirelesscommunication.

Of course, the exemplary detection unit of FIG. 2 may be differentlyconfigured. Many of the components of the detector of FIG. 2 may be inbase station 14 rather than in the detection unit 12. For example, thedetection unit may simply comprise an EMG electrode 20 in wirelesscommunication with a base station 14. In such an embodiment, A-Dconversion and signal processing may occur at the base station 14. If anECG electrode 21 is included, then multiplexing may also occur at thebase station 14.

In another example, the detection unit 12 of FIG. 2 may comprise anelectrode portion having one or more of the EMG electrode 20, ECGelectrode 21 and temperature sensor 23, in wired or wirelesscommunication with a small belt-worn transceiver portion. Thetransceiver portion may include a multiplexor 24, an A-D converter 25,microprocessor 26, transceiver 27 and other components, such as memoryand input/output (I/O) devices (e.g., alarm cancel buttons and visualdisplay).

FIG. 3 illustrates an embodiment of a base station 14 that may includeone or more microprocessors 30, a power source 31, a backup power source32, one or more I/O devices 33, and various communications means, suchas an Ethernet connection 34 and transceiver 35. The base station 14 mayhave more processing and storage capability than the detection unit 12,and may include a larger electronic display for displaying EMG signalgraphs for a caregiver to review EMG signals in real-time as they arereceived from the detection unit 12 or historical EMG signals frommemory. The base station 14 may process EMG signals and other datareceived from the detection unit 12. If the base station 14 determinesthat a seizure is likely occurring, it may send an alert to a caregivervia transceiver 35.

Various devices in the apparatus of FIGS. 1-3 may communicate with eachother via wired or wireless communication. The system 10 may comprise aclient-server or other architecture, and may allow communication vianetwork 15. Of course, the system 10 may comprise more than one serverand/or client. In other embodiments, the system 10 may comprise othertypes of network architecture, such as a peer-to-peer architecture, orany combination or hybrid thereof.

FIG. 4 illustrates an exemplary method 36 of monitoring EMG and othersignals for seizure characteristics, and initiating an alarm response ifa seizure is detected. Such a method may involve collecting of EMGsignals, calculating one or more values of a seizure variable, and usingsuch seizure variable data to populate processor or memory registers. Ingeneral, one or more seizure variables and one or more registers may beincluded in data analysis. In a step 38, EMG signals and other detectoroutput signals may be collected. Output signals may be collected in asubstantially continuous manner or periodically. Output signals may beprocessed in a step 40 to obtain seizure variable data. The data valuesmay be used to populate one or more detection registers, as shown instep 42. Processing of output signals and population of detectionregisters may be executed during a defined period of time, i.e.,collection time window. At the expiration of such a collection timewindow, each detection register may transfer its contents, if any, toone or more accumulation registers (as shown in step 44), and thecontents of one or more detection registers, if any, may be cleared.After expiration of the collection time window, and after adjustment(increase or leakage) of accumulation registers, the cycle may repeatitself (as shown by line 46), i.e., detector output may be collectedduring a subsequent collection window. Periodically, a supervisoryalgorithm may analyze the contents of one or more accumulation registersto determine whether a seizure is likely occurring (step 48). If thesupervisory algorithm determines that the sum of values or a weightedsum of values in the accumulation registers exceeds a threshold then analarm protocol may be initiated (step 50). Alternatively, thesupervisory register may determine that the contents of accumulationregisters do not indicate that a seizure is likely and the system maywait for a next analysis period (step 52).

As discussed below, a supervisory algorithm may comprise a number ofsub-routines that use various seizure variable values in theaccumulation and/or detection registers. As shown by way of example inFIG. 4, methods may involve the population of individual detectionregisters with a data value and addition of such a data value toaccumulation registers (steps 38, 40, 42, and 44). A sub-method mayinclude steps involved in the population of individual detectionregisters and accumulation registers. Each sub-method may consider oneor more characteristics of the collected data and perform processanalysis on such characteristics. Individual sub-methods may include, byway of nonlimiting example, detection of signal bursts and detection ofGTC waveforms. Sub-methods may process data in the time domain, thefrequency domain, or, in some embodiments, process portions of data inboth the time domain and frequency domain. Before discussion of thoseindividual sub-methods in greater detail, it is helpful to consider somegeneral aspects of data collection, the detectors used, as well asprocessing steps, such as data filtration that may be involved invarious sub-methods. In addition, it is instructive to discuss exemplaryEMG signal data, as shown in FIGS. 5A, 5B, 6A, and 6B discussed in moredetail further herein.

As indicated in step 38 of FIG. 4, in some embodiments, detection ofseizures may be accomplished exclusively by analysis of EMG electrodedata. In other embodiments, a combination of EMG and other detectors maybe used. For example, temperature sensors, accelerometers, ECGdetectors, other detectors, or any combinations thereof, may be used.Accelerometers may, for example, be placed on a patient's extremities todetect the type of violent movement that may characterize a seizure.Similarly, ECG sensors may be used to detect raised or abnormal heartrates that may characterize a seizure. Thus, a monitoring device maydetect an epileptic seizure without the customary multitude of wiredelectrodes attached to the head, as typical with EEG. Combination of EMGelectrodes with other detectors may, for example, be used withparticularly difficult patients. Patients with an excessive amount ofloose skin or high concentrations of adipose tissue, which may affectthe stability of contact between an electrode and the skin, may beparticularly difficult to monitor. In some embodiments, an electrode maybe attached to a single muscle, and in other embodiments a combinationof two or more electrodes may be used. Electrodes may, for example, beattached to an agonist and antagonist muscle group or signals from othercombinations of different muscles may be collected.

In general, the system described herein is compatible with any type ofEMG electrode, such as, for example, surface monopolar electrodes orbipolar differential electrodes or electrodes of any suitable geometry.Such electrodes may, for example, by positioned on the surface of theskin, may or may not include application of a gel, and may, in someembodiments, be Ag/AgCl electrodes. The use of a bipolar EMG electrodearrangement, e.g., with a reference lead and two surface inputs, allowsfor the suppression of noise that is common to those inputs. That is, adifferential amplifier may be used, and a subtraction of the signalsfrom one input with respect to the other may be accomplished, and anydifferences in signal between the inputs amplified. In such an approach,signals that are common to both inputs (such as external noise) may besubstantially nullified and preferential amplification of signalsoriginating from muscle depolarization may be achieved.

An EMG signal may be collected for a given time period, e.g., a timedomain electrode signal may be collected. Time domain electrode data,may be converted to frequency data, i.e., spectral content, usingtechniques such as Fast-Fourier Transform (FFT). In reference to FIG. 4,the conversion of data between the time and frequency domain may beincluded in a processing step 40. Other aspects of data processing mayinclude smoothing data, application of one or more frequency filters,fitting data in a given region to a particular function, and otherprocessing operations

FIGS. 5A and 5B (collectively referred to as FIG. 5) provide examples ofEMG data 54 collected over a time period of about 2 seconds. The data inFIG. 5 may exemplify data collected by placing a bipolar differentialelectrode over the biceps or triceps of a patient. FIGS. 6A and 6B(collectively referred to as FIG. 6) illustrate some of the EMG data 54of FIG. 5 converted to the frequency domain. The EMG data 74 in FIG. 6may represent, for example, a one-second epoch of the EMG data 54converted to the frequency domain. For an EMG electrode, visualrepresentation of frequency domain data may also be referred to as aspectral graph.

Referring now to the time domain data for the graph of FIG. 5, thevertical axis or scale in FIG. 5A is signal amplitude, e.g., thedifferential signal between the pair of EMG electrode inputs, and thehorizontal axis or scale shows time (in FIG. 5, the time window isapproximately two seconds). In reference to any of the graphs describedherein the term amplitude may be used, and such may refer to either themagnitude of signal, or absolute value of magnitude, as may beappropriate for a given calculation. Signals collected may, for example,be rectified, and unless otherwise noted, detection of bursts asdescribed herein involves rectified signal data. As shown in FIG. 5, theamplitude (or absolute value of the amplitude) appears to experience asustained increase 62 at least three times (56, 58, and 60) during the2-second period. Such sustained increase may be indicative of what isreferred to as a burst, or signal or data burst. As discussed in moredetail below, fluctuations in time periods between suspected bursts,such as 66 or 68, may be used to calculate a baseline. Fluctuations in abaseline region, i.e., noise, may be related to a peak to peak value, aroot mean square (RMS) value or other metric. FIG. 5B illustrates aportion of the EMG data 54, namely, the region of data including burst60 and adjacent period. In FIG. 5B, a RMS noise value 72 and amplitude70 are indicated. The signal-to-noise ratio (SNR or S/N) of burst 60 is,in this example, about 4:1, i.e., amplitude 70 is about four timeslarger than the noise value 72. The EMG data of FIG. 5 is discussed infurther detail with regards to a burst detection sub-method in FIG. 7.

Referring now to the exemplary data of FIG. 6 the vertical scalerepresents the magnitude of a given frequency (which may be referred toas spectral density) and the horizontal scale is signal frequency. Notethat the spectral data in FIG. 6 indicates a curving slope withdecreasing magnitude as the frequency increases, i.e., the spectraldensity generally decreases as the frequency increases. The ratio ofspectral density at a lower frequency to the spectral density at ahigher frequency may be a seizure variable that, for any given portionof electrode data, may have an associated value. For example, for thedata shown in FIG. 6 the ratio of spectral density at a frequency ofabout 200 Hz (76) to the spectral density at about 400 Hz (78) may havea value of about 1.1.

Also, as illustrated in the expanded portion of the same data in FIG.6B, which shows at least a portion of the characteristic GTC waveform, aregion of elevated spectral density 80, i.e., a relativelyhigh-frequency “bump” between approximately 300-500 Hz, and particularlyaround 400 Hz 82 is shown. That is, the spectral density 80 at frequency82 in that region is elevated above the spectral density 84, e.g.,within a “slumped” region, approximately located at a frequency 86 ofabout 300 Hz. The term “slump region” or “slump” may in some embodimentsrefer to a portion of spectral data generally possessing the property ofhaving positive curvature, i.e., a slump region refers to a localminimum in a set of data. The term “bump region” or “bump” may in someembodiments refer to a portion of spectral data where the data generallypossesses the property of having negative curvature, i.e., a bump regionrefers to a local maximum in a set of data. To generally possess apositive or negative curvature means that local fluctuations inindividual data points may be averaged or smoothed out of the data. Thatis, neglecting local fluctuations, e.g., due to noise, a data set maypossess a property of curvature.

The ratio of spectral density at a frequency 86 to the spectral densityat a frequency 82, or slump to bump ratio, may be used as a seizurevariable. In some embodiments, the slump to bump ratio may be used as ametric for detection of a GTC waveform. However, more advanced dataanalysis techniques, e.g., looking at a greater number of data pointsand/or advanced pattern recognition algorithms, may also be used toidentify a GTC waveform. In some embodiments, a detection unit mayinclude instructions for calculation of a slump to bump ratio and a baseunit may calculate a slump to ratio and also corroborate the slump tobump calculation with more advanced pattern recognition analyses. TheEMG data of FIG. 6 and the above data features are discussed in furtherdetail with regards to a GTC waveform detection sub-method as described,for example, in FIGS. 11 and 12.

Referring back to FIG. 4, the collection of EMG data may be accomplishedwith a detection unit and that detection unit may execute an initialanalysis and processing of data. In some embodiments, if the detectionunit determines that a seizure is likely occurring, it may send data toa base station, where further processing may occur. Thus, a detectionunit, a base station or both may process EMG signals, and either or bothdevices may execute a seizure detection sub-method. Such a sub-methodmay characterize particular features of EMG data, and may, based uponsuch a characterization, direct the transfer of data between dataregisters and accumulation registers. Those aspects of sub-methods, suchas described herein in reference to FIGS. 7 and 10-13, may involveaspects of steps 38, 40, 42, 44, and 46 of method 36. A sub-method mayfeed data into a supervisory algorithm.

FIG. 7 illustrates one embodiment of a sub-method 88 which may be usedfor analysis of data bursts. In a step 90 of FIG. 7, a detection unitand/or base station may select a protocol for analysis of data bursts.The selection of an analysis protocol may, for example, be indicated ina template file. Such a template file may include instructions to choosea routine to smooth data, a routine to filter data, a routine to treatthe data in some other manner or combinations of routines thereof. Suchroutines may be executed by either the detection unit, base station orboth. The analysis protocol may include a peak detection program, which,for example, after band-pass filtering and rectification may identifyand shape a data burst, as shown in the examples of FIGS. 9A, 9B, 9C(collectively referred to as FIG. 9) and FIG. 10. Any suitable peakdetection technique may be used (e.g., continuous wavelet transform),and may in some embodiments include, for example, data smoothingtechniques (e.g., moving average filter, Savitzky-Golay filter, Gaussianfilter, Kaiser Window, various wavelet transforms, and the like),baseline correction processes (e.g., monotone minimum, linearinterpolation, Loess normalization, moving average of minima, and thelike) and peak-finding criteria (SNR, detection/intensity threshold,slopes of peaks, local maximum, shape ratio, ridge lines, model-basedcriterion, peak width, and the like).

A peak detector may have separate attack and decay rates. These ratesmay be individually adjusted. Since there frequently may be plenty ofsustained amplitude during a real burst, fear of the peak detectedsignal decaying too quickly during bursts is generally not a problem.Therefore, the decay rate may be set to decay rather quickly following aburst. Usually the time between bursts is longer than the burst itself,and so there may be no reason to speed up the decay. However, a noisespike between bursts could artificially cause the peak detector outputto jump up to a level that would make distinguishing real seizure burstsa problem. Therefore, the attack rate may be carefully controlled toprevent this from occurring.

In step 91 of the method of FIG. 7, a burst detection algorithm may beinitiated. Burst analysis may be triggered, for example, by detection ofan EMG signal having an amplitude value that meets or exceeds a burstanalysis amplitude threshold. Within the burst detection window, the EMGdata may be analyzed for elevated amplitude using, e.g., a peakdetection program. Regions of elevated amplitude may be classified aspotential bursts. For example, referring back to FIG. 5, at least threeperiods of sustained elevation of amplitude (56, 58, and 60) may beidentified in the approximately 2-second epoch. Regions of elevatedamplitude within the burst detection window may be measured foramplitude, width, and a SNR may also be determined. A portion of data,e.g., identified as a possible peak, may have amplitude associated withit, e.g., peak amplitude, median, mean or other metric may becalculated.

In step 92 of FIG. 7, EMG signal data, such as within a certain timeperiod (burst detection window), may be analyzed for bursts. Forexample, for suspected data burst 56, amplitude 62 may be measured. Aburst may have an amplitude that is elevated over surrounding portionsof data, and that elevated amplitude may extend for a period of time.That is, a burst may have a burst width, such as burst width 64. Todetermine a burst width, a leading edge of a burst and a trailing edgeof a burst may be determined. To detect the leading edge and trailingedge of a burst, changes in amplitude for successive data points may bemeasured, e.g., the rate of change of amplitude with time may becalculated. Any other suitable technique, such as those described above,may be used, as well. In some embodiments, burst width may becategorized by calculating, for a region of time, whether a thresholdminimum amplitude is met at a given probability, e.g., where a majorityof points show elevated amplitude above some threshold.

Signal to noise calculations may involve, for example, establishing abaseline by determining fluctuations in detector signal, i.e., baselinenoise, in a time period immediately prior to data in a time suspected ofcontaining bursts. For example, an EMG signal may be relatively quiet inthe time leading up to a seizure, as discussed in more detail inconnection with FIG. 25, below. That quiet period may be used toestablish a baseline.

A baseline may also be established by looking at fluctuations betweenburst periods within the same time window suspected of having bursts.For example, referring back to the EMG data of FIG. 5, data fluctuationsin time periods between suspected bursts, such as the data in the timeperiods 66 or 68, may be used to calculate a baseline. Fluctuations in abaseline region, i.e., noise, may be related to a peak to peak value, aRMS value or other suitable baseline detection metric. In FIG. 5 anexpanded region of data, i.e., the region of data including burst 60 andadjacent period, is shown in FIG. 5B, and a root mean square noise value72 and amplitude 70 are approximately indicated. The S/N of burst 60may, for example, be about four, i.e., amplitude 70 is about four foldlarger than the noise value 72.

It should be noted that the baseline established by looking atfluctuations between burst periods may be different than the baselineestablished by looking at a pre-seizure quiet time. Thus, different peakdetection algorithms may be run for each, or the same algorithm may beramped up or down with respect to baseline detection depending onwhether detecting quiet time or seizure activity. For example, abaseline detector may be a peak detector having a much longer timeconstant than a peak detector used for signal envelope generation. Thisbaseline detector may rise up to a higher level during a tonic phase butmay ramp down during a clonic phase of activity. A negative peakdetector may also be employed to ramp a baseline detector down morequickly during relatively quiet times so as to distinguish the burstsmore readily.

In step 94, the burst detection algorithm may determine if the EMGsignal data within a burst detection window meet various requirements orthresholds or other criteria to qualify regions of elevated amplitude asbursts. For example, the algorithm may determine whether one or moreregions of elevated amplitude meet requirements for amplitude, width,and time between regions of elevated amplitude to qualify as seizurebursts. For example, a sub-method for detecting bursts may detectamplitudes above a certain threshold that are closer than Y secondsapart and farther than Z seconds apart. Such requirements (or burstcriteria) may be provided in a template file. For example, referring toTable 1, the minimum S/N criteria may be pulled from the template fileand compared to the calculated value of S/N for each suspected burst.

Generally, a burst may be characterized by a sudden increase in theamplitude of the EMG electrode signal from a lower amplitude level,maintenance of that increased amplitude level for a specified minimumamount of time, return of the amplitude level to a lower level ofelectrode signal after no more than a specified maximum time, andmaintenance of the lowered amplitude level for a specified minimum time.FIG. 8A and FIG. 8B (collectively referred to as FIG. 8) illustrateexemplary model forms or envelopes of signal bursts after filtering,rectification and peak detection. Generally, the lower amplitude signallevel may not go to zero. The lower amplitude above zero is signalnoise. The ratio of the burst amplitude level to the noise level is theSNR. For example, if the signal level of the burst is 1 volt, and thenoise is 0.35 volts, then the SNR would be 1/0.35, or 2.86. In theexample of FIG. 8, the peak amplitude 120 of EMG signal data may becompared to criterion associated with peak amplitude. If the amplitude120 is greater than a minimum amplitude criterion 120 a, and less than amaximum amplitude criterion 120 b, then the ratio of peak amplitude tothe level of noise 102 may be determined and compared to a burstamplitude criterion, e.g., a SNR threshold. If the peak amplitude meetsthe SNR threshold, then the EMG signal data may qualify as a burst (orthe start of a burst) with respect to amplitude. A maximum burstamplitude requirement may be helpful in eliminating from considerationelevated amplitude EMG data caused from external noise sources that mayintroduce amplitude well above the amplitudes capable of being producedby the human body.

FIG. 8A also shows the region of elevated amplitude as having a width114. The width 114 may be compared to a minimum burst width (dashed line116) and a maximum burst width (dashed line 118). As may be seen in FIG.8B, the width 114 falls between the minimum and maximum burst widththresholds, and thus qualifies the region of elevated amplitude as aburst with respect to width. A maximum burst width requirement may behelpful in eliminating from consideration elevated amplitude EMG datathat is from voluntary muscle activity, a noise source or is caused byelectrode connectivity problems. That could help eliminate falselyidentifying real or apparent high-amplitude muscle activity as aseizure.

FIG. 8B shows examples of two successive bursts (104 and 106) separatedby a time period 108. In FIG. 8B the time between bursts 108 may, forexample be compared to criterion values associated with a minimum periodbetween successive bursts (dashed line 110) and a maximum period betweensuccessive bursts (dashed line 112). If a sufficient quantity of burstssucceed each other within the minimum and maximum time periods, thensuccessive bursts may qualify as a burst train indicative of a seizure.However, not all burst trains indicate a seizure, and a periodicityalgorithm (discussed in more detail below) may be used to furtherevaluate the likelihood that a seizure is occurring. For example,extremely regular bursts may not indicate a seizure. Sporadic bursts maynot indicate a seizure, either, or if spaced sufficiently far apart,represent minimal threat of imminent harm from seizure.

After reaching the end of the burst detection window, the burstdetection algorithm may wait for a delay period before analyzing data ina subsequent burst detection window. By adding a delay, the burstdetection algorithm may ensure that new data is analyzed. If analysis ofa burst window, or analysis of one or more successive burst detectionwindows reveals no bursts or near-bursts, then the burst detectionsub-method may pause, as seen at step 95, until the burst analysisamplitude threshold triggers activation of the sub-method.

The burst amplitude, width and periodicity values may be stored inregisters for use by a supervisory algorithm to determine the likelihoodof a seizure occurring. If the supervisory algorithm determines that aseizure is occurring, then it may declare an alarm, and cause the basestation 14 to send an alert to a caregiver.

Criterion values may, for example, be included in a template file. Morespecifically, Table 1 lists exemplary criteria that that may be includedin a template file which may be used in a sub-method for evaluation ofdata bursts. Each criterion may be a variable that may be changed toadjust the sensitivity of the seizure detection method. Of course, notall of the criteria need be used. For example, maximum burst amplitudemay be considered optional if unduly limiting for a particular patient.Likewise, additional criteria may be used. For example, if signalamplitude is sufficiently high to trigger the burst detectionsub-method, but does not quite meet the minimum burst amplitude eventhough it meets burst width criteria, then its variance from the minimumburst amplitude may be negatively weighted by a certainty valuecriterion. A certainty value criterion may be, for example, a percentagevalue. If the measured amplitude is 95% of the minimum burst amplitude,then the certainty value may be set accordingly. If successive burstshave sufficient periodicity to qualify as a burst train, thenegatively-weighted burst may be included in the train to further testperiodicity. If a certain number of negatively-weighted bursts appear inthe data, then a supervisory algorithm may lower the minimum burstamplitude thresholds to increase the sensitivity of the burst detectionmethod for the particular patient being monitored. Similar weighting maybe done with respect to signal values that do not quite meet the otherburst criteria. Certainty values may be used by the burst detectionmethod, other sub-methods described herein, and the supervisoryalgorithm.

TABLE 1 Template data for a burst detection sub-method VariableValue/unit Type Burst analysis minimum amplitude threshold XX amplitudeCriterion for initiation of burst detection algorithm Burst detectionwindow XX seconds Routine selection Delay between adjacent burstdetection XX seconds Routine selection windows Minimum burst width XXseconds Criterion for burst count Maximum burst width XX secondsCriterion for burst count Burst envelope peak detector attack rate XXRoutine selection Burst envelope peak detector decay rate XX Routineselection Minimum burst amplitude XX amplitude Criterion for burst countMaximum burst amplitude XX amplitude Criterion for burst count MinimumS/N XX Criterion for burst count Minimum period between successivebursts XX seconds Criterion for burst count Maximum period betweensuccessive bursts XX seconds Criterion for burst count Decay rate XXData feature/weighting coefficient Decay rate (S/N) modifier XX Datafeature/weighting coefficient Selection of filter protocol (if applied)XX Routine selection Selection of smoothing protocol (if applied) XXRoutine selection Calculation method XX Routine selection Baselinecalculation method XX Routine selection Coefficient (combination withsupervisory XX Weighting coefficient algorithm)For clarity, the “XX” is simply a value placeholder, and should not beconstrued to connote magnitude or precision in any way.

Referring back to FIG. 7, in a step 96, one or more detection registersmay be loaded with burst values for a detection window. For example, aburst count register may be used to contain a value corresponding to thenumber of detected bursts within the burst detection window. Forexample, if the two-second time period of FIG. 5 was a burst detectionwindow, then the EMG data within that window may be analyzed for bursts.In FIG. 5, for example, the EMG signal data shows three bursts. Thus, avalue of 3 may be stored in the burst count register. Other registersmay be used to store other burst values, such as amplitude, periodicity,width, certainty values, and so forth.

Following each burst detection cycle, e.g., analysis of a burstdetection window, the detection register may, in some embodiments, addits contents to one or more burst accumulation registers (step 97).Before analyzing the data in subsequent burst detection windows, thedetection registers may be cleared to allow storage of burst data forthe subsequent burst detection windows. The detection registers may thenbegin storing burst values during another cycle, or, in someembodiments, begin counting bursts after a certain delay period.

In some embodiments, the EMG signal data may be written to a circularbuffer in RAM in the device hardware. One advantage of such a strategymay be that less RAM is used because the processed data may store only apattern of the data, such as peak detected values, and not a point bypoint data file of full signal data. That is, a voltage (or otherelectrical parameter that reflects amplitude of the detection unit) ateach corresponding point in time need not be stored. For example, insome embodiments, only the data necessary to derive a model form such asindicated in FIG. 8A and FIG. 8B may be stored. It should be appreciatedin those figures that noise in regions between detected bursts isdepicted to be maintained at a constant level. Thus, only a calculatedvalue of the noise, e.g., such as RMS amplitude (102), may be stored andnot all of the individual fluctuations in the baseline data. Thus, thedata file in RAM may be significantly compressed. In some embodiments,as opposed to storing a compression of the data in a time window, allraw data from a given window may be stored in a circular buffer in RAM.It should thus be appreciated that an algorithm may look at any givenpreceding time window at any point in the algorithm. Such may be used,for example, to consider how any given value of EMG data has changedbetween one or more time windows.

In some embodiments, each burst may be weighted with a value that is notonly related to detection of a burst but also related to the certaintyof burst detection. Certainty values may, for example, be related to thenormalized amplitude or the ratio of the normalized amplitude todetector noise. For example, a signal burst may be characterized bytransition from approximately 100% of the normalized amplitude toapproximately 35% of the normalized amplitude. The certainty value maybe approximately 65, which number may be loaded into a register whosemaximum value could be approximately 100.

As denoted in step 97, one or more of the detection registers may addtheir contents to one or more accumulation registers. For example, aburst count detection register may add its value to the a burst countaccumulation register.

In step 98, the accumulation registers may, in addition to accepting adata value from the detection register, adjust the value of any previousdata which may be held. For example, in some embodiments, the burstcount accumulation register may hold a value that is related to thequantity of bursts collected in a preceding number of burst detectioncycles. That is, each time the burst count detection register addscontents from one cycle, the burst count accumulation register mayremove a data value that was added during some preceding cycle. Thus,the burst count accumulation register may, in some embodiments, act as amoving sum based on the sum of counts from a number of preceding burstdetection windows. In such an embodiment, the computer may store inmemory, e.g., in any number of additional registers, the appropriatedata value to add or subtract from the burst count accumulationregister. In other embodiments, at the completion of a cycle, the burstcount detection register may add any contents, e.g., value of collectedbursts, to the burst count accumulation register and then remove acertain value, i.e., it may leak at a certain rate. A leakage rate, ordecay rate as shown in Table 1, may be included in a template file andmay be adjusted to customize the burst detection sub-method to aparticular patient or patient demographic. In some embodiments, theleakage rate may be a value that is modified based upon anothercriterion. For example, the burst count accumulation register may bemodified if one or more successive burst detection windows do notcontain any bursts.

In other embodiments, the rate of decay of the burst count accumulationregister may depend upon the S/N of bursts counted in one or more giventime window. In further embodiments, the burst count accumulationregister may be modified based on how the S/N of bursts is changing.That is, the average S/N of detected bursts may be tracked, e.g., theaverage S/N value of bursts in given time windows may, at least for someperiod of time, be stored in memory, such as in a circular RAM buffer.If the S/N of bursts changes between time windows, such a change may beanalyzed, and used to modify the decay rate of the burst countaccumulation register. In general, if the S/N of bursts is increasingthe decay rate of the burst count accumulation register will drop bysome factor and if the S/N of bursts is decreasing the decay rate of theburst count accumulation register will increase by some factor. Inaddition, during step 98 the contents of the burst count accumulationregister, may decay in a manner that is dependent upon various negativeweighting factors. For example, if no bursts are detected in a cycle,such may be an indication that a seizure is not occurring, and the rateof decay of the burst count accumulation register may be adjusted.Again, to analyze data in preceding time windows, either point by pointdata or a model shape may be stored in a circular buffer of RAM in thesystem hardware. Referring back to FIG. 4, the value stored in the burstcount accumulation register is an example of one value that may beexamined with a supervisory algorithm.

In step 99, the burst detection algorithm may wait for a time periodequal to the burst detection window delay value before analyzing EMGsignal data in subsequent burst detection windows. The burst detectionregisters may be cleared in step 100 before analyzing EMG data in thenext burst detection window. In some embodiments, the burst detectionalgorithm may continue to run until it finds one or more burst detectionwindows that do not contain any bursts or near-bursts, or until thesupervisory algorithm triggers an alarm.

In general, the presence of qualified bursts, and a large value beingstored in the burst count accumulation register, may increase theprobability that a seizure event is declared. It is also an aspect ofmethods described herein, negative weighting factors may be used, forexample, with respect to signal characteristics that diminish thelikelihood that a seizure is occurring. For example, as discussed above,different negative weighting factors, such as the absence of bursts in apreceding time window, or a decreasing S/N may influence the leakagerate of an accumulation register.

FIGS. 9A, 9B and 9C illustrate another embodiment of a burst and bursttrain detection algorithm. The flowcharts of FIGS. 9A-9C show logicflow, not actual routines. In an actual routine, they would be called bythe supervisory algorithm or be scheduled as one-time passes by timerinterrupt, not infinite loops. There are two main routines, the burstdetection algorithm (FIGS. 9A and 9B), and the burst train detectionalgorithm (FIG. 9C). The burst detection algorithm looks for a burstthat meets the requirements of amplitude (both min and max) and minimumwidth. If the minimum spacing between detected bursts is too small, theburst train detection algorithm will catch it. A burst train detectionalgorithm may rely on a periodicity algorithm, as discussed below.

FIG. 9A illustrates one embodiment of a burst detection logic flow 373.Burst detection logic flow 373 may include obtaining a signal sample inthe step 374. In the step 376, the logic flow may include determining ifthe signal amplitude is greater than a maximum allowed amplitude level.If the signal amplitude is greater than the maximum allowed level, thenthe supervisory algorithm may be informed in the step 378, and the burstdetection logic flow may then start over (step 380). If in the step 376the signal amplitude is not greater than a maximum level, then the flow373 may, in the step 382, determine if the signal amplitude is greaterthan noise by at least some SNR. If the signal amplitude is greater thanthe SNR, then the burst detection logic flow 373, as shown in step 384,may include determining if it was the first time in the routine that therequired SNR level was met. If it was the first time the SNR level wasmet, then the routine may start a burst timer (step 386) and takeanother signal sample (step 374). If it was not the first time that theSNR level was met within the flow, then the flow may includedetermining, in the step 388, if the burst timer has exceeded a minimumburst duration threshold. If the burst timer has exceeded the minimumburst duration threshold, then the signal may, in the step 390, bepre-qualified as a burst. The pre-qualified burst may then be processedin the exemplary flow 393 shown in FIG. 9B as shown in the (step 392).

The burst detection logic flow 393 in FIG. 9B may include taking asignal sample (step 394). The signal sample may, for example, be asignal sample pre-qualified as a burst as described in FIG. 9A. In thestep 396, the routine may determine if the signal amplitude is greaterthan a maximum allowed amplitude level. If the signal amplitude isgreater than the maximum level, the supervisory algorithm may beinformed in the step 398, and the burst detection flows 373, 393 maystart over (step 400). That is, a new signal sample may be taken in thestep 374 of FIG. 9A. If the signal amplitude is not greater than themaximum allowed amplitude level, the routine may then includedetermining, in the step 402, if the signal amplitude is greater thanbackground by at least a SNR. If the signal amplitude is greater thannoise by at least the SNR, the routine may, in the step 404, determineif the burst duration timer has exceeded a maximum burst durationthreshold. Considering the flows 373 and 393 together, the duration ofactivation of the burst timer, which may be initiated in the step 386 ofthe flow 373, may be evaluated against each of a minimum burst durationthreshold 388 and maximum burst duration threshold 404. That is, viewingthe flows 373 and 393 together data may be compared against each of aminimum duration threshold and a maximum duration threshold. If themaximum burst duration threshold is exceeded in the step 404, as shownin the step 406, it may be deemed that the burst is too long, and theprocess may start over (as shown in step 400). If the maximum burstduration threshold is not exceeded in the step 404, a next signal samplemay be taken in the step 394. And, as long as the signal amplitude doesnot exceed the maximum allowed level in step 396, the flow 393 may loopback to the step 402 and evaluate if the signal amplitude is greaterthan noise by at least a SNR. If in the step 402, it is determined thatthe taken signal sample (which may include signal sample in any numberof loops of the flow 393) does not exceed background by the SNR level, aburst may be deemed to be over and the burst may be qualified (step408). As indicated in the step 408, a signal qualified as a burst may becharacterized by a certainty value, the center of the burst may bedetermined and the burst data may be written into a circular memorybuffer for further analysis.

In the burst train detection logic flow of FIG. 9C, data in a circularbuffer may be analyzed. In the step 410, the routine may scan thecircular buffer for the most recent burst centers. In the step 412, thelogic flow may eliminate bursts that are too close together or too farapart from consideration. In the step 414, the routine may evaluatewhether there are enough bursts within a window to qualify as a bursttrain. If enough bursts are detected, the routine may, in the step 416,qualify the scanned data in the circular buffer as a burst train,calculate a certainty value, and deliver the burst data to a supervisoryroutine for further analysis. In the step 414, if there are not enoughbursts within the burst train window the flow may exit.

In FIG. 10 an additional exemplary algorithm 113 (the periodicityalgorithm) is described that may, in some cases, act to suppress theinitiation of a seizure alarm. The periodicity algorithm accomplishesthis task by looking at the circular buffer over a time frame andexamining how regular the detected bursts were. A periodicity algorithmmay scan different data values from various time windows that the burstdetection algorithm wrote into a circular buffer, and examine theperiodicity of signal characteristics, including those that may not beindicative of a seizure.

In some embodiments, variables in the periodicity algorithm may be:

-   -   Periodicity Time Window (in seconds)    -   Minimum Average (or Standard) Deviation Allowed (percentage)

The periodicity time window variable is the period of time over whichthe periodicity algorithm scans data. For example, the periodicity timewindow may be sufficient to include some number of burst detectionwindows from the burst detection algorithm. The Deviation Allowedvariable is the minimum value of how far from a single frequency thebursts may be distributed to qualify as a seizure. If the bursts huddletoo closely around a specific frequency, for example 1 Hz, then thatburst train may not indicate a real seizure. In some embodiments, valuesfor the periodicity algorithm may be empirically selected for default.This variable could be altered based upon patient history, experience,patient modeling and learning, and/or human feedback. In someembodiments, a patient may, for example, partake in differentactivities, such as, for example brushing teeth, exercising, walking orother activities to collect data that may be used to establish defaultsfor the periodicity algorithm.

In step 115 of the exemplary method of FIG. 10, the average duration ofthe period between bursts within the periodicity time window may becalculated. In step 117, each actual duration of the value of each suchtime period may be subtracted from the average time value, and theabsolute values of the differences used to calculate, in step 119, theaverage deviation of the periods, and convert the average deviation to apercentage.

In step 121 the average deviation percentage may be compared tothreshold values such as a minimum threshold value of average deviationpercentage as indicated in FIG. 10. Such threshold values may be taughtto the system in operation and may be customized for the particularenvironment that an individual may commonly occupy.

For example, if in a periodicity time window (measuring in seconds),nine bursts were detected at the following times:

-   -   12, 13, 13.75, 14.35, 15, 15.8, 16.2, 16.5, 17.4        there would be 8 time periods between bursts. So, over a        periodicity time window including the foregoing epoch of 5.4        seconds, there were nine bursts with eight periods between        bursts. The average period may be calculated as 5.4/8=0.675        seconds per burst. The time periods between bursts are as        follows:    -   13−12=1    -   13.75−13=0.75    -   14.35−13.75=0.6    -   15−14.35=0.65    -   15.8−15=0.8    -   16.2−15.8=0.4    -   16.5−16.2=0.3    -   17.4−16.5=0.9        In this example, a simplified method allows the time around        which a burst is centered to serve as a time stamp for that        burst. In other words, each time the burst algorithm qualifies a        burst, a time stamp may be written into a circular buffer for        use by the periodicity algorithm. In other embodiments, real        burst width may be used to calculate the actual length of the        time periods between bursts. For example, if the burst occurring        at 12 seconds lasted for 0.02 seconds, then the time period        between the burst starting at 12 and the burst starting at 13        would be 0.98 seconds. The absolute value of the deviations from        the average may be calculated as follows:    -   1−0.675=0.325    -   0.75−0.675=0.075    -   0.675−0.6=0.075    -   0.675−0.65=0.025    -   0.8−0.675=0.125    -   0.675−0.4=0.275    -   0.675−0.3=0.375    -   0.9−0.675=0.225        Averaging the absolute values may be accomplished as follows:

Sum of all deviations:0.325+0.075+0.075+0.025+0.125+0.275+0.375+0.225=1.5

Average deviation: 1.5/8=0.1875

The average deviation percentage is: 0 0.1875/0.675=27.8%. That is asignificant deviation from the average and is unlikely to be artificial.If a minimum threshold value of the average deviation percentage is set,for example, to 15%, then the periodicity algorithm would declare thatconfidence is high that this is a seizure and would not vote againstdeclaring that a seizure alarm (step 123). The result may be placed in aregister for use by the supervisory algorithm.

In another simplified example, the burst train could look like this (inseconds):

-   -   17, 17.5, 18.02, 18.51, 19.04, 19.56, 20.1, 20.6, 21.13        So, over a periodicity time window including the foregoing epoch        of 4.13 seconds, there were nine bursts with eight periods        between bursts. The average period may be calculated as        4.13/8=0.51625 seconds per burst. The individual times between        bursts are as follows:    -   17.5−17=0.5    -   18.02−17.5=0.52    -   18.51−18.02=0.49    -   19.04−18.51=0.53    -   19.56−19.04=0.52    -   20.1−19.56=0.45    -   20.6−20.1=0.5    -   21.13−20.6=0.53        The absolute value of the deviations from the average are as        follows:    -   0.51625−0.5=0.01625    -   0.52−0.51625=0.00375    -   0.51625−0.49=0.02625    -   0.53−0.51625=0.01375    -   0.52−0.51625=0.00375    -   0.51625−0.45=0.06625    -   0.51625−0.5=0.01625    -   0.53−0.51625=0.01375        The sum of all deviations may be calculated as follows:    -   0.01625+0.00375+0.02625+0.01375+0.00375+0.06625+0.01625+0.01375=1.6        The average deviation is therefore: 1.6/8=0.02        The average deviation percentage in this example is thus: 0        0.02/0.51625=3.87%. This example thus shows a very regular        pattern. If the minimum threshold value of average deviation        percentage was set to 15%, then the algorithm would declare that        confidence is very low that a true seizure is occurring and        would vote against declaring a seizure alarm. The result may be        placed in a register for use by the supervisory algorithm.

Of course, standard deviation calculations may be substituted foraverage deviation calculations for a more statistically accurate result.

The supervisory algorithm may use the results of the values provided bythe periodicity algorithm. That is, in steps 123 or 125 the algorithmmay add either a positive or negative value to the supervisoryalgorithm. Therefore, as indicated in FIG. 10, the periodicity algorithmmay either vote for declaring that bursts in the periodicity time windoware indicative of a seizure (step 123) or vote for declaring that burstsin the periodicity time window are not indicative of a seizure (step125). The particular value added may depend upon comparison tothresholds in step 121. The value added to the supervisory algorithmmay, in some embodiments, depend not only on the particular decision, atstep 121, but also on the certainty in which the decision was qualified.In addition, the value added to the supervisory algorithm may depend onother features measured. For example, characteristic patterns in anenvironment may not only have a certain periodicity they may also havecertain amplitude. For example, an algorithm may learn that a certainperiod is typically identified with a certain signal amplitude and whenthose characteristics are viewed together, an additive or super-additivevalue may modulate the supervisory algorithm.

In a real seizure, the bursts can look like they are spaced evenly.However, these are generated by the body and may be only rarely evenlyspaced. Real seizures are generally characterized by some variance inthe spacing between bursts. Other sources of signals, that is, sourcesthat are not derived from seizure muscle activity, may be picked up bythe EMG electrodes. For example, mechanical vibration of the room or bedcould result in a rhythmic vibration of the arm or other muscle to whichthe electrodes are attached. This could cause signals which may bepicked up from the electrodes and may have an elevated amplitude.However, these signals may be very regular in frequency. Likewise,regular voluntary body movements, such as from brushing teeth, mayproduce bursts that look like a seizure. Whatever the source ofinterference at the electrodes that may look like bursts, theperiodicity algorithm evaluates the periodicity of pseudo-bursts asbeing too regular and therefore not indicative of a seizure.

FIG. 11 illustrates one embodiment of another sub-method that may alsocontribute a value that may be that may be examined with a supervisoryregister. In FIG. 11, one embodiment of a GTC waveform detectionalgorithm 130 is illustrated. FIG. 12 illustrates another embodiment ofa GTC waveform detection algorithm 146. As previously described, in someembodiments, the detection unit and the base station may analyze data inthe same or different ways. The embodiment of FIG. 10 may, for example,be useful as an initial screen of data, i.e., it may be used todetermine whether a data set is sent to a base station. The embodimentof FIG. 12 may, for example, involve the comparison of a spectral shapeto a large number of files stored in memory and may be executed by abase station.

In a step 132, as shown in FIG. 11, a detection unit and/or base stationmay select an analysis protocol. The selection of an analysis protocolmay, for example, be indicated in a template file. Such a template filemay include instructions to choose a routine to smooth data, a routinefor data filtration, a routine to treat the data in some other manner,or combinations of routines thereof. Such routines may be executed atvarious steps in sub-method 130. In a step 134, data may be collectedand FFT methods may be used to convert data between the time andfrequency domains. In collection of EMG data, suitable sample rates maybe used as appropriate, for example, to avoid aliasing of the frequencydomain data. In a step 136, the frequency value associated with a localminimum value and a local maximum value of the power density may bedetermined. To accomplish such, the data may typically be smoothed and aparabolic function fit to the data in a frequency region suspected ofbeing a local maximum. In attempting to find local extreme values, thesub-method may find that the EMG data does not meet criteria to beclassified as a GTC waveform. For example, the sub-method may find thatin a given region expected to show a local maximum or local minimumvalue, the data does not exhibit such behavior.

The sub-method may, if local maximum and local minimum values are found,calculate the area under the power density/frequency curve for a regionassociated with the determined local extreme values (step 138). Forexample, the program may calculate the area under a region of 10 Hzcentered on the determined local maximum and also calculate the areaunder a region of 10 Hz centered on the determined local minimum. Theratio of these areas may be calculated, i.e., a slump to bump ratio maybe calculated, in a step 140, and compared to a threshold ratio, e.g.,minimum and maximum threshold for acceptable slump to bump ratios. Ifthe slump to bump ratio is within the threshold bounds a value may beadded to a GTC detection register in a step 142. The value added to theGTC detection register, may, in some embodiments, be related to thecertainty in which the slump to bump ratio was detected. In a next step144, the value of the GTC detection register may be added to a GTCaccumulation register. That is at the completion of a cycle, i.e., aftereach GTC collection window, the GTC detection register may add anycontents, e.g., a value reflecting a detected slump to bump ratio, tothe GTC accumulation register. In some embodiments, the GTC collectionwindow may be the same as the burst detection window, i.e., the GTCwaveform detection algorithm may analyze the same data that the burstdetection algorithm analyzes. The GTC accumulation register may then bechanged by a certain value, e.g., it may leak at a certain rate.

Referring to FIG. 12, in a step 148, another embodiment of the waveformdetection algorithm may, for example, create an image in memoryrepresenting the spectral content of the EMG signal over a certainperiod of time. For example, one or more detectors may collect data overa certain time window and then that data may be converted to thefrequency domain for spectral analysis. In a step 150, the waveformdetection algorithm may evaluate the image, e.g., spectral data, andlook for a characteristic GTC waveform. Any number of spectral regions,such as a high frequency region of the spectrum may be analyzed. In astep 152, a GTC accumulation register may be populated in a manner thatdepends on how the spectral data compares to a stored GTC waveshapetemplate.

FIG. 13 illustrates one embodiment of a waveform regularity detectionalgorithm 154. Like a periodicity algorithm, a waveform regularitydetection algorithm may be used to determine if bursts are too regularin waveform to originate from seizure activity. In a step 156, theamplitude and burst width of EMG signal data during a time period may bedetermined. This may be accomplished in much the same way as describedin for the burst detection algorithm. In a step 158 a waveform may becalculated, e.g., data from a sub-period of time around a burst may beconverted to the frequency domain and a waveform calculated. Thewaveform may be calculated and compared to waveforms that were collectedfor other bursts in the time period. In some embodiments, if thosewaveforms are too uniform, e.g., identical or very similar in at leastsome characteristics, then a regularity accumulation register may beincremented. Differences between waveforms may be calculated in a mannersimilar to that of a periodicity algorithm, e.g., by determining anaverage waveform, calculating the average deviation of each waveform,and determining the percentage difference of the average deviation fromthe average waveform. If that percentage difference falls below aregularity threshold requirement (another variable), then a regularitydetection register may be populated. In succeeding detection cycles, theregularity detection register may add its contents to a regularityaccumulation register. In some embodiments, the waveform may look foruniformity within a given time period by converting data collected overthat time period to the frequency domain and detecting a spike inamplitude over a very narrow frequency range. In a step 150, if thewaveform regularity decreases then the regularity accumulation registermay decay. As previously noted, some seizure variables may eitherenhance or weigh against the declaration of an alarm. In someembodiments, the value a regularity accumulation register may serve tosuppress the declaration of an alarm. Referring back to FIG. 4, thevalues stored in either GTC accumulation register of sub-methods 130 or146, or the value stored in the regularity accumulation register, suchas described in sub-method 160, may be a value that may be used by asupervisory algorithm.

The value stored in all or some of the above referenced accumulationdetection registers, e.g., such as described in relation to FIGS. 7,11-13, and 18, or input from other algorithms, e.g., as discussed inFIG. 10, may be periodically evaluated, such as in a step 48 of FIG. 4,which describes the use of a supervisory algorithm. The supervisoryalgorithm may be the overall seizure detection program running in theprocessor of a device in the seizure detection system 10, such as thedetection unit 12 or base unit 14. Among other things, the supervisoryalgorithm may determine whether a seizure is in process. The supervisoryalgorithm may accomplish this by evaluating the conclusions of the othersub-methods or algorithms that analyze EMG signal data, and perhapsother data such as temperature or heart rate, as well. A supervisoryalgorithm may convolute data in one or more registers that correspond toseizure variables. For example, as discussed above, a sub-method may,e.g., identify a specific characteristic of data, calculate a certaintyvalue, and increment a register value. A supervisory algorithm may thentake the register values and multiply each value by a coefficient (e.g.,from zero to one) to give more weight to certain seizure variables, andthen may add all of the resultant products together. If the sum of theproducts exceeds a threshold value, then a seizure may be declared asdetected, and an alert sent accordingly. For example, an example wouldbe TOTAL=a(register 1)+b(register 2)+ . . . z(register 26). If TOTALever goes over the detection threshold, then a seizure detection may bedeclared.

FIG. 14 illustrates one embodiment of a supervisory algorithm 162. In astep 164, the supervisory algorithm may periodically evaluate one ormore of the detection and accumulation registers. That is, thesupervisory algorithm may determine the value stored in such registers.In a step 166, the supervisory algorithm may multiply, or convolute insome other manner, the value in each register by an appropriateweighting coefficient. Such weighting coefficients may, for example, beassociated with a template file. For example, table 1 indicates acoefficient that may be used to adjust the value of the burst countaccumulation register. A sum of the values in accessed detection andaccumulation registers may be added together in a step 168. In a step170, the sum determined in step 168 may be compared to an overallthreshold. If the sum is larger than the threshold, then a seizure alarmprotocol may be initiated (step 172). In some embodiments, a supervisoryalgorithm may evaluate the output of a portion of the registers. Forexample, one or more registers may be evaluated, convoluted withcoefficients, compared to a threshold, and if appropriate, an alarmprotocol may be initiated. In some embodiments, the coefficient by whichone seizure variable is modified may depend upon the value of anotherseizure variable. For example, the system may learn that when twoseizure variables are simultaneously elevated, or related in some otherway, that the system may detect a seizure with higher confidence.

FIG. 14A illustrates another embodiment of a supervisory algorithm. Asupervisory algorithm may analyze the processed EMG data with respect toa different seizure characteristics. A supervisory algorithm mayintegrate or average over time its results and continually update itsconclusions. This may serve to remove short glitches or spikes in thedata that could lead to a false positive. In the embodiment of FIG. 14A,the supervisory algorithm uses register values from some of theforegoing sub-algorithms as follows:

-   -   Burst Train Detect flag and Certainty value    -   Periodicity good or bad and Certainty value    -   GTC waveform Detect and Certainty value        In this embodiment, each sub-algorithm could produce a flag        indicating a detection, or, in the case of the periodicity, a        flag that votes against detection. Each may have a coefficient        or multiplier variable (A, B, C, D) that establishes each        sub-algorithm's importance or weight in the overall        determination of seizure declaration. As discussed above,        certainty values may range from 0 to 100%, with 100 being the        highest certainty. The supervisory algorithm uses the Certainty        Value to gauge confidence in the results of the Burst Detection        algorithm.

Generally, a Certainty value may be used by one algorithm to transmit toanother algorithm how certain the first algorithm was in its judgment.For a burst detection algorithm, for example, one metric may be theaverage SNR during the burst normalized to a max value of 50. Anothermetric may be how closely the burst looks like an ideal burst, e.g.,through waveform regularity analysis. A burst that is barely greater inwidth than the minimum may not rate as high as one 5 times wider thanthe minimum. Also, a burst that is too close to the maximum may given alower certainty value. For example, as suggested herein, a referenceburst width could originally come from empirical data from many testpatients experiencing actual seizures, and be a factory default. Later,as data from the patient is gathered, a more representative ideal widthcould be established for that patient. The rating of a burst width couldbe normalized to a max value of 50 and added to the SNR value for amaximum of 100. Other metrics could be factored in as well and eachcould be weighted differently. One example of a method of weightingwould be to normalize each to a different value:

SNR 40%

Width 35%

Amplitude 25%

A similar process for establishing certainty values could be implementedfor each sub-algorithm.

An equation that the supervisory algorithm could use to quantify thedecision process is:

Seizure_detection=A*(Burst_Train_Flag*Certainty)+B*(Periodicity_good_flag*Certainty_good)−C*(Peridodicity_bad_flag*Certainty_bad)+D(GTC_flag*Certainty_value)

If the sum is greater than a Seizure Detection Threshold variable value,then the supervisory algorithm declares a seizure. Other seizurevariables may be used, such as Seizure Length could be used to specifyhow long (time in seconds) the seizure must be in process before analarm is generated. If the sum is less than a Seizure DetectionThreshold variable value, then the supervisory algorithm may be inactivefor a period of time before re-scanning sub-method registers.

It can be seen from the above equations that if the periodicity is good,it adds to the summation with one weight. If the periodicity is bad, itsubtracts from the summation with another weight. This allows theperiodicity algorithm to strongly vote against a seizure detection if itdetermines that the EMG signals include obvious interference such asharmonics from the power mains, fluorescent lights, etc. Other inputssuch as temperature or heart rate could be added with their owncoefficients and certainty values. Sometimes heart rate can be detectedwith EMG electrodes and thus would require no more electrodes. However,dedicated electrodes for heart rate and temperature may provide bettersignals with respect to those phenomena.

An aspect of systems and methods described herein is that they may bereadily customized and adapted as more data regarding general seizurecharacteristics for a patient, or patient demographic, is collected.Such methods may use algorithms that may have a set of routines,coefficients, or other values that may be included in a modifiabletemplate file. It may, in some embodiments, also be useful that adetection system, e.g., a system that is designed to quickly detectseizures, has an accurate log of the data and also a log of thecondition of a patient. That is, for example, a detection system thathas accurately logged the event it is intended to detect and thedetection data itself (and correlated those events in time), may, asdescribed below, be optimized.

To appreciate the concept of a template file and adaptive aspects ofsystems described herein, reference may now be made to FIGS. 15 and 16.FIG. 15 shows at a high level, a method 174 of data collection. Such amethod may be used to optimize the detection of seizures. In method 174an initial template file may be generated or selected for an individual(step 176). Once a template is generated or selected it may be added tocomputer memory of a detection unit and/or a base station. An example ofsome data that may be included in a template file was shown in Table 1.

A number of approaches may be used for establishing an initial templatefile. In some embodiments, a patient may be monitored for a period oftime in a hospital or other controlled setting and data, such as dataderived from EMG electrode outputs, may be collected and correlated withthe presence or absence of seizures, i.e., general seizurecharacteristics for an individual may be established. From that data, anoperator or software may generate an initial template file or select anappropriate file from a list of pre-generated templates. In someembodiments, an initial template file may be obtained using historicaldata from a general patient demographic. For example, a patient may bedefined by various characteristics including, for example, anycombination of age, gender, ethnicity, weight, level of body fat, fatcontent in the arms, fat content in the legs, fitness level, or thepatient may be defined by other characteristics. The patient's medicalhistory including, for example, history of having seizures, currentmedications, or other factors may also be considered. Once a templatefile is generated or selected it may be included in computer memorywithin a detection unit and base unit and an individual may use thedetection unit in a home-setting.

In step 178 a patient while in a home-setting may collect and processEMG output or other detector output, such as using a detection unit. Itshould be noted, as indicated in FIG. 1, that a detection unit may be incommunication with a base unit, transceiver and also with a data storageunit. Thus, any portion of data may be collected, processed and alsosent to a data archive. In FIG. 15, the storage of detector data isillustrated in step 180. Any portion of the data, e.g., raw data orprocessed data may be stored. In some embodiments, data may be convertedto a model form that allows one to access the data and determine howthat data would have behaved if analyzed in another algorithm. Forexample, the noise value in periods between shaped data bursts may bestored as value and may not include a point by point data file thatincludes all fluctuations in the baseline. Bursts may themselves beshaped and this pattern may be stored. In some embodiments, data may beadded to a storage archive and more than one different template appliedto that data. That is, the data may be analyzed with any number oftemplate files and the results of that analysis stored for futurereview. In that light, the results of running different pre-generatedtemplates may be stored and not raw data or other processed data. Ofcourse, the results of running those pre-generated templates may beevaluated and it may be determined, e.g., after comparison of thoseresults with data reflecting the physical states of patients, that onetemplate, i.e., a template that was not used to monitor a patient, wouldhave in fact detected the patient's seizures in a preferred manner.

Adapting an algorithm to better detect seizures in an individual patientor patient demographic may depend not only on the organization ofdetector data but also upon corroborating information, e.g., for anygiven portion of detector data, the physical condition of the patient.That is, it may, in some embodiments, be useful to document, along withEMG or other detector data, a record of what actually occurred atcertain points in a data stream. Such information may, for example, beidentified by a caregiver, as indicated in step 182. A caregiver mayalso provide such information to a data storage facility, which maystore the information (step 184). Alternatively, one caregiver mayprovide such information to an operator who may execute an optimizationprocedure. Information provided to data storage may include, forexample, whether a suspected seizure was verified to be a seizure,whether a suspected seizure was in fact something different, thelocation of the patient when an incident occurred, severity of theseizure, time of the incident, any medical care that may have beenissued and other information as well. At least some of this informationmay also be provided by the patient or individual.

In addition, in some embodiments, a patient may also provide informationrelated to general seizure characteristics. For example, a patient mayreceive an alert from the detector unit that a seizure is in progress(step 186). An individual, if alert, and aware that they are in fact notexperiencing a seizure, may be given the option of sending a message toa caregiver and/or to a data storage unit that a false positive wasalerted by the system. In some embodiments, an individual maycommunicate the presence of a false detection by simultaneously pressingtwo buttons on an attached device, e.g., the detection unit or anotherunit. Of course, the requirement that an individual simultaneously presstwo buttons may minimize the risk that an inadvertent signal is sent.Any other suitable approach to minimize inadvertent messages may also beused. A message sent in this manner, e.g., sent to a storage facilityfrom a patient (step 188), may include a time stamp to correlate a falsepositive event with the data which initiated the false positive event.Such information may be stored in a data storage facility (step 190).

An individual may, in some embodiments, also be given the option toprovide additional information, e.g., other information that may beassociated with any false positive event, or seizure incident. Suchsupporting information may include an activity they were engaged in orthe physical location they were at when they received notification thata seizure is in progress. Also, a detector unit may, as previouslydescribed, be an input/output device, and thus, a seizure alert may besent to a detector unit, or other unit carried or worn by a patient,from a base unit. That is, if the base unit controls initiation of analarm, the base station may inform the detector unit (which isphysically near the patient) that a seizure has been detected. In someembodiments, a device including means for reporting information, such asa false positive event, to a caregiver or data storage facility may beworn around the wrist or on the belt of a patient. An operator mayaccess data in a data storage facility and organize the information 192.

A method 194 of optimizing seizure detection, and updating a templatefile, is shown in FIG. 16. In step 196 an operator may add any new data,e.g., data collected in a home-setting for a patient, to any previouslystored data for that patient, i.e., an operator may update a data file.Alternatively, an operator may add newly collected data for a patient toa body of data that is associated with a patient demographic. The systemmay in step 198, for example, use the initial template file (or acurrently used template file for that patient), and characterizedetection metrics for the system as applied to the individual's updateddata file. Metrics of the system may include listing seizure events thatwere correctly identified, seizure events that were missed, falsepositives, and in some embodiments, a determination of the severity ofan event that was considered to be a seizure. Also, for any givenreported event, e.g., a seizure incident or false positive detection,the operator may, in some embodiments, be provided with a listing of thedata in different registers at the time of the event. Such informationmay, for example, be recalculated (during optimization) from originalsignal data or from stored values. In a step 200, the operator mayexecute a computer program to select fields of information, e.g.,weighting coefficients, thresholds, criteria, and selected processingroutines, from the initial template file (or currently used template)and vary those fields. The operator may also manually select and adjustone or more fields. The system may characterize detection metrics (step202) while varying template fields and select new settings (step 204)for an updated template file. Of course, the updated template file maybe downloaded to either or both of the detection unit and base station.

One aspect of methods and apparatuses described herein is that they are,in various embodiments, able to organize information between a detectionunit and base station or between those units and a data archive. Inaddition, some embodiments may be used to organize the collection ofportions of data that are most relevant.

In some embodiments, the rate at which data may be collected may dependupon whether or not an electrode is in a given state, such as an activestate, resting state, or engaged in a polling operation. For example,FIG. 17 illustrates one embodiment of a method 206 of detecting seizuresin which the rate of data collection depends upon the state of anelectrode. Method 206 may, for example, be used to toggle a detectionunit and/or base station between a “sleep” mode, i.e., characterized byoperations within dashed line 208, and a mode of substantiallycontinuous operation, such as active state 214. As shown in FIG. 17, adetector and/or base unit may be configured to exist in the restingstate 200 for a portion of time while in a “sleep mode.” While in theresting state 210 a detector or base unit may be silent, e.g., it maynot be monitoring or collecting data from a patient. The resting statemay include instructions to periodically exit the resting state 210 and,for example, collect detector data for a period of time. That is, adetector may enter a polling operation step 212 where data is collected.The duration of an individual polling operation may be sufficient tocollect data as needed to make a decision regarding the state of anelectrode. That is, for example, based on data collected during pollingstep 212 a detector may revert back to the resting state 210 or mayenter another state, such as active state 214.

Any of various routines may be used to collect data for toggling betweena resting and active state. An amplitude detection algorithm may, forexample, be used to switch an electrode between a resting and activestate. FIG. 18 illustrates one embodiment of an amplitude detectionalgorithm 216. An EMG signal amplitude may be, for example, a peakvalue, a mean value, a median value, an integrated value, or other valuethat may be measured at a given time point or over a selected timeinterval. EMG signal amplitude may be normalized or calibrated for apatient's baseline activity. As shown in FIG. 18, in a step 218 one ormore electrodes in a resting state may “wake up” and measure the EMGsignal amplitude. For example, as illustrated in step 220, if theamplitude is above a threshold level, then the one or more electrodesmay continue to measure the EMG signal amplitude and if the thresholdlevel is not obtained, the one or more electrodes may return to aresting state. By having a period of time in which a detection unit isin “sleep” mode, a system may conserve battery life, minimize the amountof data that is stored in memory, minimize the amount of data that istransferred over a network, or serve other functions. In someembodiments, a decision to enter an active state, and monitor a patientin a more continuous manner, may be made based on factors in addition toamplitude detection.

Additional embodiments that may be used to allocate data collectionamong devices are shown in FIGS. 19 and 20. In the embodiment of FIG.19, an EMG electrode in a detection unit detects an EMG signal,determines the spectral content of the signal, and may compare thespectral content to a model GTC waveform stored in the detection unit'smemory. If the spectral content is substantially similar to the GTCwaveform, then the detector unit may send approximately tenseconds-worth of EMG signal to the base station. Preferably, the sentEMG signal includes the signal that formed the basis of the comparison.The base station may independently determine the spectral content of thereceived signal, and compare the spectral content to the GTC waveformstored at the base station. If the spectral content is substantiallysimilar to the GTC waveform, then the base station may send an alert toa remote station or caregiver. Thus, in one embodiment, for an alert tobe sent, both the detection unit and base station must each determinethat the spectral content of the EMG signal is substantially similar tothe GTC waveform.

In the embodiment of FIG. 20, an EMG electrode in a detection unitdetects an EMG signal, determines the spectral content of the signal,and compares the spectral content to the GTC waveform stored in thedetection unit. If the spectral content is substantially similar to theGTC waveform, then the detector unit may send approximately tenseconds-worth of EMG signal to the base station. Preferably, the sentEMG signal includes the signal that formed the basis of the comparison.The base station may independently determine the spectral content of thereceived signal, and compare the spectral content to the GTC waveformstored at the base station. The base station may also analyze thereceived signal for burst activity, as described above, such as regularperiodicity, to determine if burst thresholds are met. If the spectralcontent is substantially similar to the GTC waveform, and the basestation recognizes burst activity that meets the burst thresholds, thenthe base station may send an alert to a remote station or caregiver.

Similarly, processing of EMG signal data for various seizure variablevalues may be accomplished at the detection unit, at the base station,or both, depending on processor existence and capability and storagecapacity.

Some additional processing techniques that may be used in the abovealgorithms or in other sub-methods are described below. For example, insome embodiments, a register may be populated in a manner such thelevel, or value of the contents, of the register is related to the timethat a seizure variable may be above threshold, related to the magnitudeof a certain characteristic of data, e.g., seizure variable, or both.For example, a register may be loaded with a set numerical value every Xseconds that a certain characteristic is maintained above a threshold.Thus, if a given number of time periods, e.g., nX seconds, aremaintained with the characteristic above threshold, the method mayadvocate a seizure detection. If the characteristic drops belowthreshold, the register may be reset or decremented in some manner. Insuch an embodiment, an alarm may be triggered based on the number oftime periods that a certain characteristic is above threshold. Aregister (e.g., a first register) may also be loaded with a numericalvalue every X seconds that a certain characteristic is above athreshold, and that numerical value may be proportional to the magnitudeof signal or number of events detected over the provided time period. Atthe completion of every X seconds, a second register may be populated ina manner that depends upon the first register, e.g., whether it ismaintained above a certain level. In such an embodiment, an alarm may betriggered, for example, if the second register is populated for acertain number of consecutive time periods. The first register may, insome embodiments decrement at a certain rate. For example, the firstregister may be loaded every X seconds in a manner proportional to themagnitude or number of registered events and also decremented each Xsecond period. Thus, the first register may either increase in value ordecrease in value as dependent upon how it is incremented ordecremented. In some embodiments, an alarm may be triggered if eitherthe second register exceeds a certain threshold, if the first registerexceeds threshold, or if either or both exceeds a certain threshold. Ifa characteristic evaluated is of a type where an integration calculationis needed, then the method may increment the register a specific amountevery X seconds. If the register is set to decay more slowly than therate of increment, then the register value will increase over time. Aslower rate of increase may allow the method to slowly build up to ahigher confidence level of seizure detection.

In some embodiments, an EMG electrode in a detection unit may detect anEMG signal, determine the spectral content of the signal, and comparethe spectral content to the GTC waveform stored in the detection unit.If the spectral content is substantially similar to the GTC waveform,then the detector unit may send an alert to the base station, a remotestation, and or caregiver. The detector unit may send the alert withoutrequiring corroborative analysis by the base station. In yet otherembodiments, the detector unit may further analyze the EMG signal forseizure burst activity, as described above, such as regular periodicity,to determine if burst thresholds are met. If the spectral content issubstantially similar to the GTC waveform, and the detector unitrecognizes burst activity that meets the burst thresholds, then thedetector unit may send an alert to a base station, a remote stationand/or caregiver.

In some embodiments, the seizure detection system may be provided with ageneralized GTC waveform and calibrated for a patient's baselineactivity, e.g., sleeping, daytime activity, etc. When waveform activityincreases, the seizure detection system may compare the signalscollected by the detection unit to the generalized GTC waveform. Theseizure detection system may begin to characterize the signals and lookfor elevated signal amplitudes. The seizure detection system may processthe signals to generate spectral content by well understood methods suchas Fast-Fourier Transform (FFT). The seizure detection system may applyfiltering to more clearly reveal higher-frequency “bursts.” The seizuredetection system may determine if the processed signal fits thegeneralized seizure characteristics by measuring one or more of thefactors of amplitude, count, time length of train, and periodicity ofbursts and comparing those factors against stored patterns andthresholds. If the thresholds are exceeded, then an alarm may be sent,e.g., to the base station together with data. The base station mayseparately process the data for verification of the alarm condition. Ifthe base station agrees with the alarm, then the base station maygenerate an alarm to remote devices and local sound generators. An alarmmay comprise an audible signal, or a text message, or email, or triggervibration in a PDA, or other suitable attention-getting mechanisms. Insome embodiments, having the base station agree to the detection unit'salarm introduces a voting mechanism for reducing false alarms. Bothdevices must vote on the decision and agree to sound the alarm. This maybe used to limit false alarms. Of course, a processor in apatient-mounted unit may process the EMG signals based on burstdetection, and may separately process the EMG signals based on GTCwaveform, and may send an alert if both processes indicate that an alarmprotocol should be initiated. Thus, voting may occur within a device, aswell.

In some embodiments, during or after a seizure event, a human operatormay review and adjust thresholds based upon the severity of the seizureor possibly the non-detection of an actual seizure because of highthresholds. Many people have seizures and do not realize that they had aseizure, e.g., the short-lived seizures discussed above. Having thisdata to review may help medically manage the person with seizures. Also,a human operator may evaluate the data and conclude that a seizure didnot occur, and either cancel the alarm or instruct the seizure detectionsystem that the detected waveform did not indicate a seizure. Likewise,a human operator may instruct the seizure detection system that anundetected seizure had occurred by, e.g., specifying the time duringwhich the seizure occurred. For example, the graphs in the figuresdiscussed above may comprise a rolling “window” of EMG activity, and thehuman operator may “rewind” the recorded signal and indicate to theseizure detection system the time window in which the seizure occurred.In some embodiments, the base unit may include a visual display thatallows display of EMG signals in time and spectral domain to allow acaregiver to view historical seizure data. In some embodiments, the basestation may visually depict the signal and provide a graphic userinterface (GUI) that allows human operators to accomplish the “window”selection and define other operating thresholds and conditions. Forexample, the system 10 of FIG. 1 may include a video camera that recordsthe patient while sleeping to allow a caregiver to review the EMG signalin coordination with video footage to assess a patient's conditioncorresponding that EMG signal. Thus, video data may be stored along withEMG signal data, and reviewed, for example, on the base station GUIalong with the EMG signal graphs. In other words, the base station couldallow a caregiver to view EMG signal graphs and corresponding video dataside-by-side. The seizure detection system may thus have additional datapoints against which to evaluate future seizure events for thatparticular patient. The seizure detection system may employs adaptivelyintelligent software to “learn” the patient's seizure patterns, and overtime effectively customize the generalized GTC waveform to better detectseizures in that patient.

An apparatus for detecting seizures is preferably man-portable, and mayinclude a detection unit that may be attached to the body, such as byuse of an elastic arm band. The detection unit may be battery powered,and may wirelessly communicate with the base station. The detection unitmay include sufficient data storage, processing and transmissioncapability to receive, buffer, process and transmit signals. Thedetection unit may process the signals and conduct a simplifiedcomparison, e.g., using two factors of amplitude and frequency, with thegeneralized seizure detection requirements stored in the detection unit.When the detection unit determines that a seizure is occurring, it candownload both its analysis and the raw signal data to a bedside basestation for more complex processing. The base station may have much morepower, larger storage capability and greater processing speed and power,and be better able overall to process the information. It could have alarger database of patterns to compare against. As the seizure detectionsystem “learns” the patient's patterns, the base station may modify thegeneralized seizure detection requirements to more closely model thepatient's pattern. The base station may update the detection deviceperiodically with the modified generalized seizure detectionrequirements. Likewise, the base station may transmit raw and processedsignal data to a remote computer for further analysis and aggregationwith signal data from other units in use. For example, multiple basestations may transmit data for multiple patients to a remote computer.Each base station may not receive the other base station's data, but theremote computer may serve as a common repository for data. Aggregationof the data may allow further data points upon which to further refinethe generalized seizure detection requirements, thresholds andstatistical information that may be supplied to base stations anddetection units as a factory default.

As previously noted, in some embodiments, in addition to using EMG,electrocardiography (ECG) may be used to corroborate (or contradict) theoccurrence of a seizure. This option could be used with particularlydifficult patients. Patients with an excessive amount of loose skin orhigh concentrations of adipose tissue may be particularly difficult tomonitor. For example, a factor associated with reliable EMGmeasurements, is the stability of the contact between the electrodes andskin. For some patients this may be difficult to achieve in a reliablemanner ECG data may be included in a method for determining a likelihoodof whether a seizure related incident is taking place (or has takenplace) and ECG data may be used to determine whether a seizure should bedeclared, e.g., an alarm initiated. Moreover, skin and fat areinherently a type of frequency filter.

Heart rate may, for example, elevate during a seizure, e.g., a patientmay become tachycardic. As discussed further herein, if the EMGprocessing portion of the seizure detection apparatus determines that aseizure may be in progress and the heart rate does not go up, then theconfidence of the detection may be reduced. For example, epilepticpatients that use a beta blocker drug may not experience a rise in heartrate. In such situations, a method incorporating heart rate as a factormay be provided with a coefficient to lower the weight given to thatfactor. Thus, the disclosed detection method and apparatus may beadjusted or readily customized according to patient-specificconsiderations, such as use of a particular drug regimen. In someembodiments, ECG may be used to detect other cardiac dysrhythmia, suchas bradycardia or asystole following a seizure, and to send an alarm ifsuch a condition is detected. Data from a temperature sensor situated asto detect patient temperature may also be used to corroborate occurrenceof a seizure or to initiate an alarm.

Generally, the devices of a seizure detection system may be of anysuitable type and configuration to accomplish one or more of the methodsand goals disclosed herein. For example, a server may comprise one ormore computers or programs that respond to commands or requests from oneor more other computers or programs, or clients. The client devices, maycomprise one or more computers or programs that issue commands orrequests for service provided by one or more other computers orprograms, or servers. The various devices in FIG. 1, e.g., 12, 13, 14,16, 17, 18 and/or 19, may be servers or clients depending on theirfunction and configuration. Servers and/or clients may variously be orreside on, for example, mainframe computers, desktop computers, PDAs,smartphones (such as Apple's iPhone™, Motorola's Atrix™ 4 G, andResearch In Motion's Blackberry™ devices), tablets, netbooks, portablecomputers, portable media players with network communicationcapabilities (such as Microsoft's Zune HD™ and Apple's iPod Touch™devices), cameras with network communication capabilities, wearablecomputers, and the like.

A computer may be any device capable of accepting input, processing theinput according to a program, and producing output. A computer maycomprise, for example, a processor, memory and network connectioncapability. Computers may be of a variety of classes, such assupercomputers, mainframes, workstations, microcomputers, PDAs andsmartphones, according to the computer's size, speed, cost andabilities. Computers may be stationary or portable, and may beprogrammed for a variety of functions, such as cellular telephony, mediarecordation and playback, data transfer, web browsing, data processing,data query, process automation, video conferencing, artificialintelligence, and much more.

A program may comprise any sequence of instructions, such as analgorithm, whether in a form that can be executed by a computer (objectcode), in a form that can be read by humans (source code), or otherwise.A program may comprise or call one or more data structures andvariables. A program may be embodied in hardware or software, or acombination thereof. A program may be created using any suitableprogramming language, such as C, C++, Java, Perl, PHP, Ruby, SQL, andothers. Computer software may comprise one or more programs and relateddata. Examples of computer software include system software (such asoperating system software, device drivers and utilities), middleware(such as web servers, data access software and enterprise messagingsoftware), application software (such as databases, video games andmedia players), firmware (such as device specific software installed oncalculators, keyboards and mobile phones), and programming tools (suchas debuggers, compilers and text editors).

Memory may comprise any computer-readable medium in which informationcan be temporarily or permanently stored and retrieved. Examples ofmemory include various types of RAM and ROM, such as SRAM, DRAM, Z-RAM,flash, optical disks, magnetic tape, punch cards, EEPROM. Memory may bevirtualized, and may be provided in, or across one or more devicesand/or geographic locations, such as RAID technology.

An I/O device may comprise any hardware that can be used to provideinformation to and/or receive information from a computer. Exemplary I/Odevices include disk drives, keyboards, video display screens, mousepointers, printers, card readers, scanners (such as barcode,fingerprint, iris, QR code, and other types of scanners), RFID devices,tape drives, touch screens, cameras, movement sensors, network cards,storage devices, microphones, audio speakers, styli and transducers, andassociated interfaces and drivers.

A network may comprise a cellular network, the Internet, intranet, localarea network (LAN), wide area network (WAN), Metropolitan Area Network(MAN), other types of area networks, cable television network, satellitenetwork, telephone network, public networks, private networks, wired orwireless networks, virtual, switched, routed, fully connected, and anycombination and subnetwork thereof. The network may use a variety ofnetwork devices, such as routers, bridges, switches, hubs, repeaters,converters, receivers, proxies, firewalls, translators and the like.Network connections may be wired or wireless, and may use multiplexers,network interface cards, modems, IDSN terminal adapters, line drivers,and the like. The network may comprise any suitable topology, such aspoint-to-point, bus, star, tree, mesh, ring and any combination orhybrid thereof.

Wireless technology may take many forms such as person-to-personwireless, person-to-stationary receiving device, person-to-a-remotealerting device using one or more of the available wireless technologysuch as ISM band devices, WiFi, Bluetooth, cell phone SMS, cellular(CDMA2000, WCDMA, etc.), WiMAX, WLAN, and the like.

Communication in and among computers, I/O devices and network devicesmay be accomplished using a variety of protocols. Protocols may include,for example, signaling, error detection and correction, data formattingand address mapping. For example, protocols may be provided according tothe seven-layer Open Systems Interconnection model (OSI model), or theTCP/IP model.

Although the foregoing specific details describe certain embodiments ofthis invention, persons reasonably skilled in the art will recognizethat various changes may be made in the details of this inventionwithout departing from the spirit and scope of the invention as definedin the appended claims and considering the doctrine of equivalents.Therefore, it should be understood that this invention is not to belimited to the specific details shown and described herein.

Additional information related to the methods and apparatus hereindescribed may be understood in connection with the examples providedbelow.

EXAMPLES Example 1

In one example, a patient who may be susceptible to having seizures maybe monitored. The patient may, for example, be monitored during a periodimmediately following a hospitalization, or at some other time wherethey are at risk for SUDEP. It may be useful to set up the monitoringprotocol for the patient, based at least in part, upon data obtained forthe patient while the patient is monitored for seizures in a controlledsetting. For example, during hospitalization the patient may bemonitored and data may be collected for determining general seizurecharacteristics. The patient may, for example, be monitored with EMGover a period of several days, or some other interval, as necessary tocollect data associated with a statistically significant number ofseizures. During the period of hospitalization, the patient EMG data maybe collected by placing bipolar differential electrodes on or near oneor more pairs of muscles, e.g., agonist and antagonist muscle pairs. EMGdata may, for example, be collected from a first group of muscles, e.g.,the biceps and triceps, and a second group of muscles, e.g., thehamstrings and quadriceps. EMG data from time periods with knownseizures and also intervals with non-seizure periods may be collected,archived and an operator may analyze the data.

An operator may analyze the data and characterize how the patient datarelates to a seizure variable, including, for example, seizure variablescharacteristic of a burst. An operator may, for example, measure theamplitude, width, and determine the signal to noise (S/N) ratio forportions of data that are elevated, i.e., periods that may becharacterized as data bursts. Signal to noise calculations may involve,establishing a baseline by determining fluctuations in detector signal,i.e., baseline noise, in a time period immediately prior to data in atime suspected of containing bursts. Various filters may be applied tothe data, e.g., digitized data may be subjected to a 3rd orderButterworth filter from 300 Hz to 500 Hz or filtered in another manner.Using data that is filtered, the operator may, for example, repeatmeasurement of amplitude, width, and signal to noise (S/N) ratio fordata at times that appears to contain data bursts. The operator may thenselect threshold values associated with burst measurements.Alternatively, an operator may opt to use threshold values typical forall patients or patients of a certain demographic.

Similarly, an operator may, for example, determine the frequencyposition of local minimum values and local maximum values of powerdensity for the spectral data. For example, data from a certain timewindow, such as five seconds, may be collected and converted to spectraldata (in the frequency domain). The operator may determine local maximumand minimum values and specify a range of frequencies on either side ofthe local maximum value and local minimum value and an algorithm maycalculate the area under the power density/frequency curves. The ratioof these areas may be used as the value of a seizure variable, e.g., aslump to bump ratio. A threshold value for the slump to bump ratio maybe specified by the operator or selected from a template file for allpatients, or patients of a certain demographic.

An operator may import archived data, i.e., data from periods collectedin which a seizure was present and other non-seizure periods, into acomputer program using the selected threshold values and instructionsfor executing an algorithm. The algorithm may, for a given time window,e.g., 5 seconds, calculate values of burst related seizure variables.For example, for any time period, software may detect possible bursts,and may also measure amplitude, width and S/N. If bursts meet thecriteria established, e.g., are within the set thresholds, the computermay populate a value in a burst detection register. To clarify the flowof data in the algorithm, model data from Example 1 may be referenced toFIG. 21. FIG. 21 shows how model data in a procedure (270) for analysisof data bursts may be organized, and how data may be transferred betweena detection register in computer memory and an accumulation register,also in computer memory. In a first interval of time (271), data may beanalyzed, and for example, it may be determined that three events meetthreshold requirements for characterization as bursts. In a step (272)data may be transferred to a detection register. The detection register(273) in FIG. 21 is represented by dashed line (273), and the flow ofinformation within the detection register (273) is represented by blocks(274, 275, 277, and 278), which represent the detection register (273)in different states. As data is transferred in step (272), the detectionregister in a state (274), i.e., storing a data value of zero, maybecome populated with a value of three, as shown in state (275). In astep (279), the data value stored in the detect register (273) may betransferred to an accumulation register (280). In FIG. 21 the bursttrain accumulation register (280) is represented by dashed line (280),and the flow of information within the burst train accumulation register(280) is represented by blocks (281, 282, 284, and 285), which representthe accumulation register in different states. In step (279), theaccumulation register in a state (281), i.e., a store storing a datavalue of zero, may become populated with a value of three, as reflectedin state (282). Referring back to the detection register (273), upontransferring contents to the accumulation register (280), in step (276),the detection register (273) may clear its contents, as reflected instate (277). As reflected in step (286), in a second interval of time(286), another interval of data may be analyzed, and for example, it maybe determined that five events meet threshold requirements and arecharacterized as bursts. In step (287), the detect register (273), nowin state (277) may receive data associated with the measured burst valuefrom step (287), i.e., a value of five. The detect register (273) maynow hold a data value of five, as shown by state (278). Prior totransfer of data from the detect register, i.e., in state (278) to theburst train accumulation register (280), the burst train accumulationregister (280), may be subjected to an adjust accumulation register step(283). That is, in step (283) the burst train accumulation register maybe adjusted in value. For example, as illustrated in Example 1, theaccumulation register is shown to “leak” a value of one during theadjust accumulation register step (283). Thus, if step 283 denotes aleakage value of one and if a greater number of bursts are detected insuccessive time intervals, e.g., steps (271) and (286), then theaccumulation register will increase in value. For example, as shown inExample 1, in step (288), the detect register (273) transfers itscontents to the burst train accumulation register (280), while the bursttrain accumulation register is in state (284), and a value content offive is transferred to the burst train accumulation register (280). Theaccumulation register would then hold a data value of seven, as shownfor state (285).

In addition to the steps above, an algorithm may also involve otherregisters, e.g., a GTC accumulation register. For example, as describedin relation to FIG. 22, a GTC accumulation register (290) may bepopulated. Thus, it should be appreciated that at any point in time, theburst train accumulation register (280) and the GTC accumulationregister (290) may hold a value. A supervisory algorithm (162), may beused to analyze the data in those registers (285) and (290). To clarifythe flow of data in Example 1, reference is now made to FIG. 22, as wellas the general description of supervisory algorithms in FIG. 14.

As shown in FIG. 22, and using illustrative data for this Example 1, theGTC accumulation register (290) is shown to have a value of five. Theburst train accumulation detection register (280) is shown to be in astate (285), and as noted previously, holds a value of seven. In a step291 of the supervisory algorithm, the values of the registers aremultiplied by a coefficient. That coefficient may be pulled from atemplate file and used as a weighting factor for associated seizurevariables. That is, as shown in Example 1, a GTC weighting coefficient(298) may be 1.5 and a burst coefficient (299) may have a value of 1.0.The, weighted value of the two seizure variables followingmultiplication with their associated coefficients may then be 7.5 (292)and 7 (293). In a step (294) those values may be added together, and asshown in FIG. 22, a sum value, e.g., 14.5, may become associated with asupervisory register (295). In a step (296) the value of held in thesupervisory register (295) may be compared to a threshold value. Forexample, a threshold value for reporting a seizure may be 14, and thus,an alarm protocol would be triggered.

In Example 1, the data that is input into the algorithm is historicaldata from a patient's time in the hospital. Thus, the operator may instep (297) compare the results determined by the algorithm to the actualstate of the patient at the time that the data was collected. That is,an operator may compare the result that would have been initiated withthe actual course that was appropriate. An operator may thus compare,for all of the data that is available, how accurately the algorithmdetects actual seizures and whether the algorithm would have detectedany false positives, e.g., decisions to declare an alarm when the propercourse of action was to not report a seizure incident.

The computer program may allow the operator to manually adjustcoefficients, including for example threshold values for burst or GTCwaveform detection (such as slump to bump), GTC coefficient (298), burstcoefficient (299), or combinations thereof. The program may be set toautomatically adjust any combination of the aforementioned coefficientsin an optimization routine, wherein the computer may modify thecoefficients and look for an ideal combination that provides bothaccurately detects seizures and also minimizes false positivedetections.

The patient in Example 1 may be sent home and monitored with aconfiguration of EMG electrodes that closely resembles the configurationof EMG electrodes used to optimize the detection algorithm. As thepatient is monitored, data may be collected and the presence of anydetected seizures, missed seizures (if present), and false positives maybe reported. The system may periodically analyze the available archiveddata, including any archived data derived while the patient is at home,and re-optimize a combination of coefficients. Thus, the system mayadapt to better monitor a given patient over time.

Example 2

In this Example 2, a patient may be set up to be monitored in a homesetting using a pair of EMG electrodes on the biceps and triceps. Thepatient may be set up to be monitored based on a template file forpatients that share a demographic with the patient. In Example 2, thepatient may be an obese male and an initial set of coefficients andthresholds may be used to monitor the patient based on a set ofcoefficients and thresholds optimized for the entire set of data fromall obese males for which data is available. As distinguished, fromExample 1, the patient in this example may be monitored without previousevaluation in a hospital setting. That is, the patient may be monitoredwith weighting coefficients derived entirely by importing valuesassociated with other patients, e.g., patients that sharecharacteristics with the patient. The patient in Example 2 may bemonitored for several weeks and the system may record electrode data.For the model data in Example 2, the system may accurately detect fiveseizure events but miss one seizure event. The system may then beoptimized with archived data from the patient. That is, data from thepatient may be used to adjust coefficients to improve the accuracy ofdetecting all events.

Example 3

In FIG. 23, the top trace labeled “EMG1-raw” shows EMG electricalactivity using a bipolar EMG electrode arrangement. The trace labeled“EMG2-raw” is from a similar bipolar electrode arrangement (differentialelectrode) on the triceps of the same arm. The vertical scale in theFIG. 23 graphs, EMG1-raw and EMG2-raw, is signal amplitude, e.g., thedifferential signal between either the pair of EMG electrode inputs onthe biceps or the differential signal between the pair of EMG electrodeinputs on the triceps, and the horizontal scale shows time (in FIG. 23,the time window is approximately 4 h28′55″ to approximately 4 h29′00″).FIG. 23 shows the collection of 5 seconds worth of patient data. In someembodiments, data may be collected over some other time period.Attachment of EMG electrodes on opposing muscle groups, e.g., such asthe biceps and triceps, may be beneficial for several reasons. Forexample, as further discussed below, an electrode configuration thatinvolves opposing muscles may be useful in the interpretation of datawherein a patient is involved in certain activities, e.g., non-seizuremotion, and differentiation of data collected while the patient isengaged in such activity from electrode data collected while the patientis experiencing a seizure.

Still referring to FIG. 23, the bottom left graph (labeled “EMG1Spectral Analysis”) is a representation of the frequencies of datacollected from the EMG electrode over the biceps (spectral content). Thebottom right graph (labeled “EMG2 Spectral Analysis”) is arepresentation of the frequencies of the triceps EMG electrode. Datacollected over a given time period, i.e., time domain electrode data,may be converted to frequency data, i.e., spectral content, usingtechniques such as Fast-Fourier Transform (FFT). For the spectral data,the horizontal scale is signal frequency, and the vertical scale is thesignal amplitude, which for the spectral data described herein may bereferred to as the spectral density. Note that the spectral data in FIG.23 indicates a curving slope with decreasing amplitude as the frequencyincreases, i.e., the spectral density generally decreases as thefrequency increases. The ratio of spectral density at low frequencies tothe spectral density at higher frequencies is a seizure variable that,for any given set of electrode data, may have an associated value. Forexample, for the data shown in FIG. 23 the ratio of spectral density ata frequency of about 200 Hz (298) to the spectral density at about 400Hz (300) may have a value of about 5.0. The ratio of spectral densitiesat those frequencies, or at other frequencies, may be a seizure variableand the value of that seizure variable, such as derived from data inFIG. 23, may be generally characteristic of non-seizure muscle activity,such as moving in bed or moving arms. In some cases, such as in FIG. 6,where the ratio at 200 Hz to 400 Hz is lower, such a ratio may beindicative of seizure activity.

Example 4

FIG. 24 provides a spectral graph of EMG signals at a different windowof time than those of FIG. 23, namely, from approximately 4 h39′30″ toapproximately 4 h39′35″ when the patient is again non-seizure moving.The spectral graph shows a high spectral density across a wide group offrequencies in the frequency band. Some normal voluntary muscle movementis a coordinated contraction of agonist and antagonistic muscles in acooperative way to achieve a particular motion. In contrast to FIG. 23,and to illustrate the coordination of different muscle groups, in FIG.24, the data in “EMG1-raw” and the data in “EMG2-raw” are from differentelectrodes associated with an agonist and antagonist muscle group, i.e.,data from those muscles are superimposed upon each other. In someembodiments, the coordination of signals between electrodes on agonistand antagonist muscles may be used as a negative weighting factor fordetection of a seizure. Often during seizures this coordination is lost.Instead, the muscles tend to lock up with muscles fighting each other. Agood example of a scenario wherein coordination of agonist andantagonist muscles is lost may be seen in the tonic phase of a motorseizure when the biceps and triceps muscles are both stimulated. Thesemuscles will fight each other with very high amplitude signals but thearms may not move much at all. That is, data traces from differentelectrodes where a phase relationship is maintained for some period oftime may be evidence that an individual is not experiencing a seizure.

Example 5

The data shown in FIGS. 25-27 are collectively indicative of howelectrode data may change as patients transition from a non-seizurestate to the experiencing of an actual seizure. FIG. 25 shows arelatively quiet time (from time approximately 7 h20′40″ toapproximately 7 h20′45″) of EMG signals obtained during sleep just priorto a seizure. The spectral graph shows only relatively low frequencyactivity. The amplitude of electrode data at the far right of the timedomain graph (later times), e.g., the amplitude at a point (304), isincreased over data illustrated at earlier times, e.g., the amplitude ata point (302). That is, the amplitude of electrode data is increasing asthe seizure approaches. In some embodiments, achieving a signalamplitude may trigger a change in state for an EMG electrode or initiatetransfer of data between a detector and base unit and/or data storageunit. Changing states for detectors from sleep to active is discussedabove. Achieving an amplitude at a point (304), or achieving such anamplitude with a certain frequency for data points over a certainperiod, e.g., such as a one second interval (306), may be used as acriteria that initiates the transfer of data between a detection unitand base unit and/or data archive.

FIG. 26 shows the EMG signals recorded during sleep at the onset of aseizure (showing time approximately 7 h21′00″ to approximately 7h21′05″). The two lower spectral graphs (“EMG 1 Spectral Analysis” and“EMG2 Spectral Analysis”) show a minor “bump” (308) (with poor signal tonoise) in the spectral display at the higher frequencies, betweenapproximately 350-450 Hz, and a minor “slump” (310) in the spectraldisplay at lower frequencies, between about 250-350 Hz. In brief, thedata in FIG. 4 shows the beginning structure of a “GTC waveform,” whichis shown in FIG. 5 more clearly. However, at first, during a seizure,electrode data derived from muscles, e.g., muscles whose activity is ina process of building up, during a seizure may show the “GTC waveform”only poorly (if at all), and while the spectral density is greater athigher frequencies than typically seen for non-seizure data, such data,at the start of a seizure, may seem random or show only minor variationsin spectral density across high frequency regions. Some electricalsignals associated with normal voluntary muscle activity, recorded withmacro-electrodes are almost entirely below 300 Hz. However, electricalfrequencies recorded with macro-electrodes frequently extend above 300Hz in a sustained manner during a seizure with motor manifestations. Insome embodiments, the duration of time in which a threshold spectraldensity is achieved, e.g., at some high frequency, may be a seizurevariable.

FIG. 27 shows the evolution of the EMG signals as the seizure progresses(showing time approximately 7 h21′20″ to approximately 7 h21′25″). Asmay be seen in the bottom right spectral graph, which corresponds to thetriceps electrode, the characteristic GTC waveform shows a region ofelevated spectral density, i.e., a relatively high-frequency “bump”between approximately 300-500 Hz, and particularly around 400 Hz. Thatis, the spectral density at a point (312) in that region is elevatedabove the spectral density (314), e.g., within a “slumped” region,approximately located within a range of about 250 Hz to 350 Hz. Theratio of spectral density at the point (312) to the spectral density atthe point (314), or slump to bump ratio, may be used as a seizurevariable. In comparison of the spectral graph in FIGS. 26 and 27 itshould be noted that as the patient begins to transition into a seizurethat the GTC waveform changes. For example, a measurable slump to bumpratio becomes present in FIG. 27. As the ratio becomes measurable, a GTCdetection register may become populated with an increasing value. If theGTC detection register becomes populated with a value that is greaterthan the leakage rate of the GTC accumulation register the value in theGTC accumulation register may increase over successive time periods.

In some embodiments, the slump to bump ratio may be used as a metric fordetection of a GTC waveform. However, more advanced data analysistechniques, e.g., looking at a greater number of data points and/oradvanced pattern recognition algorithms, may also be used to identify aGTC waveform. For example, in some embodiments a detection unit mayinclude instructions for calculation of a slump to bump ratio and a baseunit may calculate a slump to ratio and also corroborate the slump tobump calculation with more advanced pattern recognition analyses.

For this patient, the EMG data bursts have significant noise, i.e.,large statistical fluctuations, at time points between them. Otherpatients may have less noise, resulting in GTC waveforms that are moreclearly visible, and slump to bump ratios with greater signal to noise.A variety of analysis techniques may be used to improve the signal tonoise for detection of a GTC waveform and/or slump to bump ratio. Forexample, in some embodiments, spectral data over a certain frequencyrange may be integrated, e.g., the area of the spectral curve within afrequency range of a “bump region” may be calculated. Also, the area ofthe curve within a frequency range of a “slump region” may becalculated. The specific ranges for slump to bump used for integrationmay be optimized for a given patient. That is, historical electrode datamay be accessed from a data repository, different ranges for the slumpregion and/or the bump region may be selected, and different values forthe slump to bump calculated for each selected ranges. Some slump tobump ratios, e.g., selected with some ranges, may show better S/N ratiosand/or better correlation with the presence of a seizure than a slump tobump calculated with other ranges. That is, general seizurecharacteristics for the slump to bump ratio using frequency data in onerange may prove to be more useful, i.e., show better correlation withthe presence of a seizure, than a slump to bump ratio using anotherfrequency range. Thus, a slump to bump seizure variable may be optimizedfor a given patient and may be updated periodically as historical datais collected for the patient.

In some embodiments, data in a predetermined frequency range, e.g., arange for a patient that typically shows a slump, may be smoothed andthe local minimum in the data established. The area under a curveapproximately centered on the local minimum may be calculated.Similarly, the algorithm may analyze data in another predeterminedfrequency range, e.g., a range for a patient that typically shows abump. Data in that range may be smoothed, a local maximum established,and the area under the curve approximately centered on the local maximummay be calculated. The area under the local minimum, area under thelocal maximum, and ratio of those integrals may be used as seizurevariables. In some embodiments, a detector unit may perform acalculation of the slump to bump ratio for a given portion of electrodedata and a base station may perform more advanced pattern recognitiontechniques on the electrode data.

Example 6

In Example 6, and associated FIGS. 28-31, some aspects of data filteringare described. FIG. 28 illustrates additional EMG data for the samepatient also during a seizure. In this embodiment, the EMG 2 signal attime approximately 7 h22′50″ to approximately 7 h22′55″ has beenfiltered with a 3rd order Butterworth filter from 300 Hz to 500 Hz. Whenfiltering is applied to the EMG 2 signal, the time domain data shows aseries of bursts, i.e., regions of elevated EMG signal amplitudeseparated by lower amplitude signals, with high signal to noise. Forexample, at least four different burst regions (316, 318, 320, and 322)may be detected in the data of FIG. 28. The bursts shown in FIG. 28 maybe categorized based upon the number of bursts, e.g., such as four,within a time window, the period between adjacent bursts (324, 326, and328) and the time duration of a burst (330). Such burst features may beseizure variables. Referring now to the spectral graphs in FIG. 28,application of a high frequency filter in this embodiment, clearlyillustrates the presence of high intensity frequency data. FIG. 28 alsoshows sharp, brief frequency “spikes” in the bottom two graphs. Thosespikes may generally correspond to noise from overhead lighting athousehold frequency of 60 Hz, and may generally appear at 60 Hzharmonics. Such interferences may be recognized and an algorithm mayinclude instructions to disregard such data signatures. Also, the EMG1signal (biceps) shows sustained contraction (tonic activity), and theEMG2 signal (triceps) shows periodic contraction (clonic activity).Thus, and in contrast to the data illustrated in FIG. 27, such agonistand antagonist muscle groups do not necessarily have a correlated phasebetween them.

The lower right graph of FIG. 29 in particular shows even moredramatically how filtering from 350 Hz to 450 Hz, in the EMG 2 signal,can reveal bursts (332, 334, and 336) and high frequency information(338) out of the electrode signal (showing time approximately 7 h22′10″to approximately 7 h22′15″). The selection of a given filter may in someembodiments be adjusted for a given patient.

FIG. 30 shows the exact same frame as FIG. 29, except the EMG 2 signalis unfiltered. It is evident from the spectral display that the lowerfrequencies have a higher amplitude as compared to the data in FIG. 29.Furthermore, bursts associated with the time domain data clearly havemuch lower signal to noise ratios. Based on the data in FIG. 29 and FIG.30, it should be appreciated that electrode data may be filtered in anyof various ways. The value of a given seizure variable may be determinedfrom data collected using a filter that improves the signal to noise ofthe calculated value. For example, burst width and burst count may becollected from an electrode that uses a filter, such as a 3rd orderButterworth filter from 300 Hz to 500 Hz (FIG. 28) or a filter from 350Hz to 450 Hz (FIG. 29). Other seizure variables, such as the slump tobump ratio of a GTC waveform may be collected without use of a filter orwith another filter, such as one that passes a lower range offrequencies, as shown in FIG. 30. As shown in FIG. 30 a slump region(340) and a bump region (342) may be detected.

FIG. 31 provides another good example of increasing the discriminationof seizure bursts for the EMG 2 signal with respect to the noise (timeapproximately 7 h25′22″ to approximately 7 h25′27″), e.g., increasingthe signal to noise ratio of the spectral data by filtering the rawdata. For example, representative burst (344) shows a high signal tonoise ratio. Note the relative irregularity of the bursts (344, 346, and348), as shown in the time domain data, which may be a factor that tendsto indicate a seizure. That is, the periods between adjacent bursts,such as burst interval (350) and burst interval (352), have differentvalues. In FIG. 31 the EMG 1 data, which has not been filtered, shows acharacteristic GTC waveform, with a detectable slump (354) and bump(356).

Example 7

In Example 7, and associated FIGS. 32-34, some aspects of data that may,for example, include features that may apply negative weighting todetection algorithm are discussed. FIG. 32 may indicate a short-livedseizure preceding the foregoing seizure (time approximately 5 h17′41″ toapproximately 5 h17′46″). Several bursts (358, 360, and 362) appear tohave occurred, and are evident in both the EMG 1 and EMG 2 signals.Those bursts may be of relatively low concern due to their shortduration. Some patients experience many of these short seizures.Comparison, of such short bursts with archived data, e.g., historicaldata for such patients, may be used to modify, e.g., a minimum burstdetection width criteria. Thus, the algorithm may adapt to selectivelyneglect some data features, i.e., short and inconsequential bursts, andthe algorithm may become better adapted to avoid initiation ofunnecessary alarms.

FIG. 33 provides an example of high amplitude signals even after the EMG2 signal has been filtered (time approximately 5 h15′46 to approximately5 h15′51″). As the upper two waveforms show (“EMG1-raw” and “EMG2-raw”),the signals are highly uniform, a characteristic that may be detectedand may be used to assess that the data may not indicate a seizure. Thebursts are also very close together (the burst period is too small).Such a characteristic may also be detected and used to qualify the dataand weigh against a determination that a seizure may be occurring. Insome embodiments, either the signal uniformity or time period betweenregions of elevated amplitude may be used to disqualify data events ormay be used to apply a negative weight to a seizure variable, e.g.,amplitude bursts. Data that is highly uniform or has too short a periodbetween data events may indicate an interfering signal, such as from anearby electrical device. In real seizures, huge spikes at severaldiscrete frequencies are rare or nonexistent. Again, historical data maybe collected for a patient and analyzed. Coefficients may be adjusted toadapt the algorithm and avoid initiation of unnecessary alarms.

FIG. 34 (time approximately 4 h39′36″ to approximately 4 h39′40″)provides another example of sustained signals that may not trigger analarm because they are too uniform and/or have too short a periodbetween repeating data events. Such characteristics may be attributed toexternal noise and are typically not associated with a seizure.

Example 8

In example 8, and associated FIGS. 35 and 36, data from another patientwho exhibits data bursts is shown. Here, as well, a differential bipolarelectrode with two inputs was placed over the person's biceps (graph notshown), and also over the persons triceps (upper graph labeled“EMG2-raw”). The vertical scale shows the amplitude of the signal. Themiddle graph (labeled “EMG2 filtered 350-450) shows the signal of theupper graph filtered to show 350-450 Hz frequencies. Note how welldefined the bursts are, e.g., representative bursts (364) and (366), andhow well the 350-450 Hz filtering works to reveal the characteristic GTCwaveform, as seen in the middle graph and in the lower right graph(labeled “EMG2 Spectral Analysis”). The period of the bursts is fairlyregular but not the same from burst to burst. In that light, it shouldbe appreciated that while some seizures show fairly regular periodicity,real seizures are subject to fluctuations that are greater than somesources of noise, e.g., from man-made sources or from voluntary muscleactivity. The balance between near perfect regularity for an artificialsource of noise and the periodicity of burst trains may be balanced foran individual patient, such as by varying coefficients and thresholdvariables in a periodicity algorithm.

FIG. 36 continues the waveform of this patient, and shows how wellordered, but not completely uniform, a series of bursts (368, 370, and372) may be. This pattern may be typical for some patients and mayprovide a very characteristic pattern that may be assigned very highweight in an algorithm.

Although the disclosed method and apparatus and their advantages havebeen described in detail, it should be understood that various changes,substitutions and alterations can be made herein without departing fromthe invention as defined by the appended claims. Moreover, the scope ofthe present application is not intended to be limited to the particularembodiments of the process, machine, manufacture, composition, ormatter, means, methods and steps described in the specification. As onewill readily appreciate from the disclosure, processes, machines,manufacture, compositions of matter, means, methods, or steps, presentlyexisting or later to be developed that perform substantially the samefunction or achieve substantially the same result as the correspondingembodiments described herein may be utilized. Accordingly, the appendedclaims are intended to include within their scope such processes,machines, manufacture, compositions of matter, means, methods or steps.

1. An apparatus for detecting seizures with motor manifestations, theapparatus comprising: one or more EMG electrodes capable of providing anEMG signal substantially representing seizure-related muscle activity;and a processor configured to receive the EMG signal, process the EMGsignal to determine whether a seizure may be occurring, and generate analert if a seizure is determined to be occurring based on the EMGsignal.
 2. The apparatus of claim 1 further comprising one or more of anECG electrode, a temperature sensor or an accelerometer.
 3. Theapparatus of claim 1, wherein the one or more EMG electrodes are mountedto one or more of an arm band, adhesive tape, or item of clothing so asto allow positioning of the one or more EMG electrodes over a muscle. 4.The apparatus of claim 1, wherein the one or more EMG electrodes aredifferential bipolar electrodes.
 5. The apparatus of claim 1 comprisingtwo EMG electrodes capable of being associated with anagonist/antagonist muscle pair, wherein one EMG electrode is associatedwith an agonist muscle, and the other EMG electrode is associated withits antagonist muscle.
 6. The apparatus of claim 5 wherein theagonist/antagonist muscle pair comprises the triceps brachii and bicepsbrachii.
 7. The apparatus of claim 1, further comprising a transceiverfor transmitting the alert.
 8. The apparatus of claim 1, furthercomprising a base station in communication with the processor forreceiving the alert.
 9. The apparatus of claim 8, wherein the basestation further comprises an I/O device capable of allowing manualadjustment of alert settings and visually displaying the EMG signal ordata based thereon.
 10. The apparatus of claim 1, wherein the processoris capable of processing the EMG signal to determine whether a seizuremay be occurring by detecting the existence of two or more bursts. 11.The apparatus of claim 10, wherein the processor is capable ofprocessing the EMG signal to determine whether a seizure may beoccurring by detecting the periodicity of the two or more bursts. 12.The apparatus of claim 10, wherein the processor is capable ofprocessing the EMG signal to determine whether a seizure may beoccurring by detecting the regularity of the two or more bursts.
 13. Theapparatus of claim 1, wherein the processor is capable of processing theEMG signal to determine whether a seizure may be occurring by detectingthe existence of a GTC waveform.
 14. The apparatus of claim 13, whereinthe GTC waveform comprises a frequency-domain bump between approximately300 Hz and approximately 500 Hz.
 15. The apparatus of claim 13, whereinthe GTC waveform comprises a frequency-domain bump between approximately350 Hz and approximately 450 Hz.
 16. The apparatus of claim 1 whereinthe processor is configured to send the alert to one or more remotereceivers.
 17. The apparatus of claim 16 wherein one of the remotereceivers is a cellular telephone.
 18. The apparatus of claim 1, furthercomprising a remote data storage unit capable of receiving and storingthe EMG signal or data based thereon.
 19. The apparatus of claim 1,further comprising one or more leads-off detectors configured toindicate whether one or more of the EMG electrodes is sufficiently closeto a muscle to provide a substantially accurate EMG signal representingactivity of the muscle.
 20. The apparatus of claim 1, wherein the one ormore EMG electrodes and processor are packaged as a single unitmountable to a human body. 21-78. (canceled)